Técnicas de minería de datos supervisadas en el espacio de vida de adultos mayores de la ciudad de Manizales

dc.contributor.advisorDuque Méndez, Darío
dc.contributor.authorPérez Trujillo, Manuel Alejandro
dc.contributor.researchgroupGrupo de Ambientes Inteligentes y Adaptativos (GAIA)spa
dc.coverage.countryManizales, Colombia
dc.date.accessioned2022-02-01T13:56:42Z
dc.date.available2022-02-01T13:56:42Z
dc.date.issued2021
dc.descriptionfiguras, tablasspa
dc.description.abstractLa tendencia de aprovechar al máximo los datos para obtener conocimiento que permita la toma de decisiones está posicionándose en diferentes ámbitos. Para el caso del área médica, la analítica asociada a la población adulta mayor se considera un campo amplio de oportunidades. Diferentes fuentes de información de adultos mayores se encuentran disponibles para ser accedidas y analizadas para objetivos específicos. Esta investigación propone la identificación de conocimiento nuevo asociado a la movilidad, específicamente al Life-Space Assessment (LSA) en adultos mayores de la ciudad de Manizales a través de minería de datos. Específicamente se identificaron las variables con mayor relación con respecto al espacio de vida restringido (LSA<60). El análisis se llevó a cabo desde un estudio transversal y uno longitudinal. La propuesta de minería estuvo acompañada de etapas de imputación, normalización, reducción de dimensionalidad y entrenamiento y testeo de algoritmos supervisados. Variables asociadas a la depresión y ejecución física tuvieron alta importancia en la clasificación de la restricción del espacio de vida. Variables asociadas con la violencia se agregan al conocimiento del LSA. Estos resultados pueden soportar políticas públicas y tomas de decisiones en Manizales que beneficien a los adultos mayores. (Texto tomado de la fuente)spa
dc.description.abstractTendency for take advantage of data to obtain knowledge that allow taking decision is taking relevance in different areas. In the case of medical area, analytics associated with elderly population is considered a large field of opportunities. Different sources of information about elderly people are available to be used and analyzed for specific targets. This investigation proposes identification of new knowledge associated with mobility, specifically with Life-Space Assessment (LSA) in elderly people of Manizales city through data mining. Specifically, was identified variables with mayor relationship with respect to restricted life space (LSA<60). Analysis was executed from a cross-sectional and longitudinal study. The proposal of data mining was accompanied of imputation, normalization, dimensionality reduction, training and testing supervised algorithms stages. Variables associated to depression and physical execution had high relevance in classification of restricted life space. Variables associated with violence was added to LSA knowledge. These results can put up with public policy and taking decisions in Manizales that benefit elderly people.eng
dc.description.curricularareaDepartamento Informática y Computaciónspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Administración de Sistemas Informáticosspa
dc.description.methods1. Revisión y selección de técnicas de minería de datos 2. Implementación de técnicas de minería de datos en el dataset y hallazgo de conocimiento relacionado con movilidad 3. Validación del conocimiento con expertosspa
dc.description.researchareaMinería y análisis de datosspa
dc.format.extentxviii, 150 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80826
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.departmentDepartamento de Informática y Computaciónspa
dc.publisher.facultyFacultad de Administraciónspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Administración - Maestría en Administración de Sistemas Informáticosspa
dc.relation.referencesAl-Aidaroos, K. M., Abu Bakar, A., & Othman, Z. (2010). Naïve Bayes variants in classification learning. Proceedings - 2010 International Conference on Information Retrieval and Knowledge Management: Exploring the Invisible World, CAMP’10, 276–281. https://doi.org/10.1109/INFRKM.2010.5466902eng
dc.relation.referencesAl Snih, S., Peek, K., Sawyer, P., Markides, K., Allman, R., & Ottenbacher, K. (2012). Life-Space Mobility Among Mexican Americans Aged 75 Years and Older. Journal of the American Geriatrics Society, 60(3), 532–537. https://doi.org/10.1161/ATVBAHA.114.303112.ApoA-Ieng
dc.relation.referencesAllman, R. M., Sawyer, P., & Roseman, J. M. (2006). The UAB study of aging: Background and insights into life-space mobility among older Americans in rural and urban settings. Aging Health, 2(3), 417–429. https://doi.org/10.2217/1745509X.2.3.417eng
dc.relation.referencesAlom, Z., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network.eng
dc.relation.referencesAlshebber, K. M., Dunlap, P. M., & Whitney, S. L. (2020). Reliability and Concurrent Validity of Life Space Assessment in Individuals with Vestibular Disorders. Journal of Neurologic Physical Therapy, 44(3), 214–219. https://doi.org/10.1097/NPT.0000000000000320eng
dc.relation.referencesAngela McCrone, Smith, A., Hooper, J., Parker, R. A., & Peters, A. (2019). The LifeSpace Assessment Measure of Functional Mobility Has Utility in Community-Based Physical Therapist Practice in the United Kingdom. Physical Therapy, 53(9), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004eng
dc.relation.referencesAnsari, Z., H, Q. M., & Abdullah, A. (2019). Performance Research on Medical Data Classification using Traditional and Soft Computing Techniques. International Journal of Recent Technology and Engineering, 2, 990–995.eng
dc.relation.referencesArtzi, M., Bressler, I., & Bashat, D. Ben. (2019). Differentiation Between Glioblastoma, Brain Metastasis and Subtypes Using Radiomics Analysis. Journal of Magnetic Resonance Imaging Magnetic, 1–10. https://doi.org/10.1002/jmri.26643eng
dc.relation.referencesArvanitakis, Z., Shah, R. C., & Bennett, D. A. (2019). Diagnosis and Management of Dementia: Review. JAMA - Journal of the American Medical Association, 322(16), 1589–1599. https://doi.org/10.1001/jama.2019.4782eng
dc.relation.referencesAuais, M., Alvarado, B. E., Curcio, C.-L., Garcia, A., Ylli, A., & Deshpande, N. (2016). Fear of falling as a risk factor of mobility disability in older people at five diverse sites of the IMIAS study. Archives of Gerontology and Geriatrics, 66, 147–153. https://doi.org/10.1016/j.archger.2016.05.012eng
dc.relation.referencesAuais, M., Alvarado, B., Guerra, R., Curcio, C., Freeman, E. E., Ylli, A., Guralnik, J., & Deshpande, N. (2017). Fear of falling and its association with life-space mobility of older adults: A cross-sectional analysis using data from five international sites. Age and Ageing, 46(3), 459–465. https://doi.org/10.1093/ageing/afw239eng
dc.relation.referencesBajwa, M. N., Malik, M. I., Siddiqui, S. A., Dengel, A., Shafait, F., Neumeier, W., & Ahmed, S. (2019). Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Medical Informatics and Decision Making, 8, 1–16.eng
dc.relation.referencesBaker, P. S., Bodner, E. V., & Allman, R. M. (2003). Measuring Life-Space Mobility in Community-Dwelling Older Adults. Journal of the American Geriatrics Society, 51(11), 1610–1614. https://doi.org/10.1046/j.1532-5415.2003.51512.xeng
dc.relation.referencesBanerjee, I., Ling, Y., Chen, M. C., Hasan, S. A., Langlotz, C. P., Moradzadeh, N., Chapman, B., Amrhein, T., Mong, D., Rubin, D. L., Farri, O., & Lungren, M. P. (2018). Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classificatio. Artificial Intelligence In Medicine, August, 0–1. https://doi.org/10.1016/j.artmed.2018.11.004eng
dc.relation.referencesBarnes, L. L., Wilson, R. S., Bienias, J. L., Mendes De Leon, C. F., Kim, H. J. N., Buchman, A. S., & Bennett, D. A. (2007). Correlates of life space in a volunteer cohort of older adults. Experimental Aging Research, 33(1), 77–93. https://doi.org/10.1080/03610730601006420eng
dc.relation.referencesBéland, F., Julien, D., Bier, N., Desrosiers, J., Kergoat, M. J., & Demers, L. (2018). Association between cognitive function and life-space mobility in older adults: Results from the FRéLE longitudinal study. BMC Geriatrics, 18(1). https://doi.org/10.1186/s12877-018-0908-yeng
dc.relation.referencesBen-Assuli, O., Heart, T., Shlomo, N., & Klempfner, R. (2019). Bringing big data analytics closer to practice : A methodological explanation and demonstration of classification algorithms. Health Policy and Technology, 8(1), 7–13. https://doi.org/10.1016/j.hlpt.2018.12.003eng
dc.relation.referencesBentley, J. P., Brown, C. J., McGwin, G., Sawyer, P., Allman, R. M., & Roth, D. L. (2013). Functional status, life-space mobility, and quality of life: a longitudinal mediation analysis. Quality of Life Research, 22(7), 1621–1632. https://doi.org/10.1007/s11136-012-0315-3eng
dc.relation.referencesBerchtold, S., Keim, D. A., & Kriegel, H. P. (1996). The X-tree: An Index Structure for High-Dimensional Data. In Proceedings ot the 22nd VLDB Conference. Academic Press. https://doi.org/10.1016/B978-1-55860-651-7.50124-8eng
dc.relation.referencesBernal, M. C., Curcio, C. L., Chacón, J. A., Gómez, J. F., & Botero, A. M. (2001). Validez y fiabilidad de la escala de Braden para predecir riesgo de úlceras por presión en ancianos1. Revista Española de Geriatría y Gerontología, 36(5), 281–286. https://doi.org/10.1016/s0211-139x(01)74737-3spa
dc.relation.referencesBhuta, P., Himakireeti, K., & Mohammad, N. (2019). Supervised Learning Algorithms for Detection of Brain Tumour. International Journal of Innovative Technology and Exploring Engineering, 8, 1099–1102.eng
dc.relation.referencesBlagus, R., & Lusa, L. (2013). SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics, 14, 1–16. https://doi.org/10.1186/1471-2105-14-106eng
dc.relation.referencesBoyle, P. A., Buchman, A. S., Barnes, L. L., James, B. D., Bennett, D. A., & Boyle PA, Buchman AS, Barnes L, James B, B. D. (2010). Association between life space and risk of mortality in advanced age. Journal of American Geriatrics Society, 58(10), 1925–1930. https://doi.org/10.1111/j.1532-5415.2010.03058.x.eng
dc.relation.referencesBreault, J. L., Goodall, C. R., & Fos, P. J. (2002). Data mining a diabetic data warehouse. Artificial Intelligence in Medicine, 26(1–2), 37–54. https://doi.org/10.1016/S0933-3657(02)00051-9eng
dc.relation.referencesBreiman, L. (2001). Random forests. Machine Learning, 5–32. https://doi.org/10.1201/9780367816377-11eng
dc.relation.referencesBrugman, S. (2019). pandas-profiling: Exploratory Data Analysis for Python. https://github.com/pandas-profiling/pandas-profilingeng
dc.relation.referencesByles, J. E., Leigh, L., Vo, K., Forder, P., & Curryer, C. (2015). Life space and mental health: A study of older community-dwelling persons in Australia. Aging and Mental Health, 19(2), 98–106. https://doi.org/10.1080/13607863.2014.917607eng
dc.relation.referencesCalafati, R. O. (2017). Estrategias para el tratamiento de datos faltantes (“missing data”) en estudios con datos longitudinales [Universitat Oberta de Catalunya]. http://openaccess.uoc.edu/webapps/o2/bitstream/10609/64085/6/romancalafatiTFG0617memoria.pdfspa
dc.relation.referencesCaldas, V., Fernandes, J., Vafaei, A., Gomes, C., Costa, J., Curcio, C., & Guerra, R. O. (2020). Life-Space and Cognitive Decline in Older Adults in Different Social and Economic Contexts: Longitudinal Results from the IMIAS Study. Journal of Cross-Cultural Gerontology, 35(3), 237–254. https://doi.org/10.1007/s10823-020-09406-8eng
dc.relation.referencesCao, X. H., Stojkovic, I., & Obradovic, Z. (2016). A robust data scaling algorithm to improve classification accuracies in biomedical data. BMC Bioinformatics, 17(1), 1–10. https://doi.org/10.1186/s12859-016-1236-xeng
dc.relation.referencesCerda, J., Vera, C., & Rada, G. (2013). Odds ratio: Aspectos teóricos y prácticos. Revista Medica de Chile, 141(10), 1329–1335. https://doi.org/10.4067/S0034-98872013001000014spa
dc.relation.referencesCerda, P., Varoquaux, G., & Kégl, B. (2018). Similarity encoding for learning with dirty categorical variables. Machine Learning, 107(8–10), 1477–1494. https://doi.org/10.1007/s10994-018-5724-2eng
dc.relation.referencesChambon, S., Thorey, V., Arnal, P. J., Mignot, E., & Gramfort, A. (2019). DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal. Journal of Neuroscience Methods. https://doi.org/10.1016/j.jneumeth.2019.03.017eng
dc.relation.referencesChatterjee, A., Woodruff, H., Wu, G., & Lambin, P. (2021). Limitations of Only Reporting the Odds Ratio in the Age of Precision Medicine: A Deterministic Simulation Study. Frontiers in Medicine, 8(May), 1–4. https://doi.org/10.3389/fmed.2021.640854eng
dc.relation.referencesChawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 321–357. https://doi.org/10.1613/jair.953eng
dc.relation.referencesChui, K. T., Alhalabi, W., Pang, S. S. H., de Pablos, P. O., Liu, R. W., & Zhao, M. (2017). Disease diagnosis in smart healthcare: Innovation, technologies and applications. Sustainability (Switzerland), 9(12), 1–23. https://doi.org/10.3390/su9122309eng
dc.relation.referencesClarke, P., & Gallagher, N. A. (2013). Optimizing mobility in later life: The role of the urban built environment for older adults aging in place. Journal of Urban Health, 90(6), 997–1009. https://doi.org/10.1007/s11524-013-9800-4eng
dc.relation.referencesCohen-Mansfield, J., Shmotkin, D., & Hazan, H. (2010). The effect of homebound status on older persons. Journal of the American Geriatrics Society, 58(12), 2358–2362. https://doi.org/10.1111/j.1532-5415.2010.03172.xeng
dc.relation.referencesCui, Song, Id, Q. W., West, J., & Id, J. B. (2019). Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease. PLoS Comput Biol, 1–25.eng
dc.relation.referencesCui, Sunan, Luo, Y., Tseng, H., Haken, R. K. Ten, & Naqa, I. El. (2019). Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage. 46(May), 2497–2511. https://doi.org/10.1002/mp.13497eng
dc.relation.referencesCurcio, C. L., Alvarado, B. E., Gomez, F., Guerra, R., Guralnik, J., & Zunzunegui, M. V. (2013). Life-Space Assessment scale to assess mobility: Validation in Latin American older women and men. Aging Clinical and Experimental Research, 25(5), 553–560. https://doi.org/10.1007/s40520-013-0121-yeng
dc.relation.referencesCurcio, Carmen-Lucia, Alvarado, E., Gomez, F., Guralnik, J., & Victoria, M. (2013). Life-Space Assessment scale to assess mobility: Validation in Latin American older women and men. Aging - Clinical and Experimental Research. https://doi.org/10.1007/s40520-013-0121-yeng
dc.relation.referencesCurcio, Carmen-Lucía, Benjumea, Á., & Gómez, F. (2018). FRAILTY AND LIFE SPACE : RESULTS FROM IMIAS STUDY. International Conference on Frailty & Sarcopenia Research 2017, April 2017.eng
dc.relation.referencesCurcio, Carmen-Lucia, Henao, G.-M., & Gomez, F. (2014). Frailty among rural elderly adults. BMC Geriatrics, 14(1), 2. https://doi.org/10.1186/1471-2318-14-2eng
dc.relation.referencesCurcio, Carmen-Lucia, Wu, Y. Y., Vafaei, A., Fernandez, J., Barbosa, D. S., Guerra, R., Guralnik, J., & Gomez, F. (2019). A Regression Tree for Identifying Risk Factors for Fear of Falling : The International Mobility in Aging Study ( IMIAS ). XX(Xx), 1–8. https://doi.org/10.1093/gerona/glz002eng
dc.relation.referencesDas, D., Ito, J., Kadowaki, T., & Tsuda, K. (2019). An interpretable machine learning model for diagnosis of Alzheimer’s disease. 2050, 1–18. https://doi.org/10.7717/peerj.6543eng
dc.relation.referencesDavenport, S. J., Paynter, S., & de Morton, N. A. (2008). What instruments have been used to assess the mobility of community-dwelling older adults? Physical Therapy Reviews, 13(5), 345–354. https://doi.org/10.1179/174328813X13789827565589eng
dc.relation.referencesDe Vet, H., Terwee, C., Mokkink, L., & Knol, D. (2011). Field-testing : item reduction and data structure. https://doi.org/10.1017/CBO9780511996214.005eng
dc.relation.referencesDeist, T. M., Dankers, F. J. W. M., Valdes, G., Wijsman, R., Hsu, I., Oberije, C., Lustberg, T., Soest, J. Van, Hoebers, F., Jochems, A., Naqa, I. El, Wee, L., Morin, O., David, R., Bots, W., Kaanders, J. H., Belderbos, J., Solberg, T., Monshouwer, R., … Lambin, P. (2018). Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers. Medical Physics. https://doi.org/10.1002/mp.12967eng
dc.relation.referencesDelgado Enríquez, L. P., Jaramillo Ortegón, D. P., Salazar Gil, V., Vieira Silva, J. G., González Marín, A. del P., Castellanos Ruiz, J., & Vergara Quintero, M. del C. (2017). El Adulto Mayor de Manizales - Consideraciones para una propuesta de Política Pública sobre Envejecimiento y Vejez (U. A. de Manizales (ed.)).spa
dc.relation.referencesDepartment of Economic and Social Affairs of the United Nations. (2019). World Population Ageing 2019. In Economic and Social Affairs, Population Division. United Nations. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Report.pdfeng
dc.relation.referencesDesjardins, J. (2019). How much data is generated each day? World Economic Forum. https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/eng
dc.relation.referencesDurairaj, M., & Nandha Kumar, R. (2013). Data Mining Application on IVF Data For The Selection of Influential Parameters on Fertility. International Journal of Engineering and Advanced Technology, 2(6), 262–266. http://www.ijeat.org/attachments/File/v2i6/F2068082613.pdfeng
dc.relation.referencesEbenuwa, S. H., & Sharif, M. H. D. S. (2019). Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data. IEEE Access, 7, 24649–24666. https://doi.org/10.1109/ACCESS.2019.2899578eng
dc.relation.referencesFairhall, N., Sherrington, C., Kurrle, S. E., Lord, S. R., Lockwood, K., & Cameron, I. D. (2012). Effect of a multifactorial interdisciplinary intervention on mobility-related disability in frail older people: randomised controlled trial. BMC Medicine, 10. https://doi.org/10.1186/1741-7015-10-120eng
dc.relation.referencesFathi, R., Bacchetti, P., Haan, M. N., Houston, T. K., Patel, K., & Ritchie, C. S. (2017). Life-Space Assessment Predicts Hospital Readmission in Home-Limited Adults. Journal of the American Geriatrics Society, 65(5), 1004–1011. https://doi.org/10.1111/jgs.14739eng
dc.relation.referencesFawagreh, K., Gaber, M. M., & Elyan, E. (2014). Random forests: From early developments to recent advancements. Systems Science and Control Engineering, 2(1), 602–609. https://doi.org/10.1080/21642583.2014.956265eng
dc.relation.referencesFayyad, U., Piatetsky-shapiro, G., & Smyth, P. (1996). Fayyad 1996 From Data Mining to Knowledge Discovery. 37–54.eng
dc.relation.referencesFernandes, J., Gomes, C. dos S., Guerra, R. O., Pirkle, C. M., Vafaei, A., Curcio, C. L., & Dornelas de Andrade, A. (2021). Frailty syndrome and risk of cardiovascular disease: Analysis from the International Mobility in Aging Study. Archives of Gerontology and Geriatrics, 92(October 2020). https://doi.org/10.1016/j.archger.2020.104279eng
dc.relation.referencesFernández, C. F. (2018). El desalentador panorama del adulto mayor en Colombia. Portafolio. https://www.portafolio.co/economia/panorama-del-adulto-mayor-en-colombia-2018-517356spa
dc.relation.referencesFontenele Garcia, I. F., Tiuganji, C. T., Simões, M. D. S. M. P., & Lunardi, A. C. (2020). Activities of daily living and life-space mobility in older adults with chronic obstructive pulmonary disease. International Journal of COPD, 15, 69–77. https://doi.org/10.2147/COPD.S230063eng
dc.relation.referencesFontenele Garcia, I. F., Tiuganji, C. T., Simões, M. do S. M. P., & Lunardi, A. C. (2018). A study of measurement properties of the Life-Space Assessment questionnaire in older adults with chronic obstructive pulmonary disease. Clinical Rehabilitation, 32(10), 1374–1382. https://doi.org/10.1177/0269215518780488eng
dc.relation.referencesGaitan Hidalgo, D. C. (2020). COVID 19 y “quedarse en casa”: un posible riesgo ante la violencia intrafamiliar. Pesquisa Javeriana. https://www.javeriana.edu.co/pesquisa/covid-19-y-quedarse-en-casa-un-posible-riesgo-ante-la-violencia-intrafamiliar/spa
dc.relation.referencesGiannouli, E., Fillekes, M. P., Mellone, S., Weibel, R., Bock, O., & Zijlstra, W. (2019). Predictors of real-life mobility in community-dwelling older adults: An exploration based on a comprehensive framework for analyzing mobility. European Review of Aging and Physical Activity, 16(1). https://doi.org/10.1186/s11556-019-0225-2eng
dc.relation.referencesGómez-Verján, J. C., & Gutiérrez-Robledo, L. M. (2018). The Challenge of Big Data and Data Mining in Aging Research. In Aging Research - Methodological Issues: Second Edition (pp. 1-246). https://doi.org/10.1007/978-3-319-95387-8eng
dc.relation.referencesGomez, F., Curcio, C. L., & Duque, G. (2011). Dizziness as a geriatric condition among rural community-dwelling older adults. Journal of Nutrition, Health and Aging, 15(6), 490–497. https://doi.org/10.1007/s12603-011-0050-4eng
dc.relation.referencesGuralnik, J. M., Simonsick, E. M., Ferrucci, L., Glynn, R. J., Berkman, L. F., Blazer, D. G., Scherr, P. A., & Wallace, R. B. (1994). A Short Physical Performance Battery Assessing Lower Extremity Function: Association With Self-Reported Disability and Prediction of Mortality and Nursing Home Admission Energetic cost of walking in older adults View project IOM committee on cognitive agi. Article in Journal of Gerontology, 49(2), 85–94. https://doi.org/10.1093/geronj/49.2.M85eng
dc.relation.referencesGutiérrez, P. A., Pérez-Ortiz, M., Sánchez-Monedero, J., & Hervás-Martínez, C. (2016). Representing ordinal input variables in the context of ordinal classification. Proceedings of the International Joint Conference on Neural Networks, 2016-Octob, 2174–2181. https://doi.org/10.1109/IJCNN.2016.7727468eng
dc.relation.referencesGuyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1–3), 389–422. https://doi.org/10.1023/A:1012487302797eng
dc.relation.referencesHand, D. J., & Adams, N. M. (2015). Data Mining. 2(January 2013), 5–20. https://doi.org/10.1017/CBO9781139058452.002eng
dc.relation.referencesHathaway, Q. A., Roth, S. M., Pinti, M. V, Sprando, D. C., Kunovac, A., Durr, A. J., Cook, C. C., Fink, G. K., Cheuvront, T. B., Grossman, J. H., Aljahli, G. A., Taylor, A. D., Giromini, A. P., Allen, J. L., & Hollander, J. M. (2019). Machine ‑ learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics. Cardiovascular Diabetology, 1–16. https://doi.org/10.1186/s12933-019-0879-0eng
dc.relation.referencesHernández Orallo, J., Ramírez Quintata, M. J., & Ferri Ramírez, C. (2004). Introducción a la minería de datos. Pearson Prentice Hall.spa
dc.relation.referencesHo, S. H., Tan, D. P. S., Tan, P. J., Ng, K. W., Lim, Z. Z. B., Ng, I. H. L., Wong, L. H., Ginting, M. L., Yuen, B., Mallya, U. J., Chong, M. S., & Wong, C. H. (2020). The development and validation of a prototype mobility tracker for assessing the life space mobility and activity participation of older adults. BMC Geriatrics, 20(1), 1–12. https://doi.org/10.1186/s12877-020-01649-xeng
dc.relation.referencesİlkim, E., Nurdan, E., Yusuf, A., Yalçın, Ö., & Çiğdem, E. (2018). THE ANALYSIS OF THE EFFECTS OF ACUTE RHEUMATIC FEVER IN CHILDHOOD ON CARDIAC DISEASE WITH DATA MINING. International Journal OfMedical Informatics. https://doi.org/10.1016/j.ijmedinf.2018.12.009eng
dc.relation.referencesInstituto Nacional de Medicina Legal y Ciencias Forenses. (2018). CARACTERIZACIÓN DE LOS SUICIDIOS MANIZALES 2017. 4 Congresso Internacional y 19 Nacional de Medicina Legal y Ciencias Forenses, 48.spa
dc.relation.referencesIravani, S., & Conrad, T. (2019). Deep Learning for Proteomics Data for Feature Selection and Classification. International Federation for Information Processing 2019, 1, 301–316.eng
dc.relation.referencesIshihara, K., Izawa, K. P., Kitamura, M., Ogawa, M., Shimogai, T., Kanejima, Y., Morisawa, T., & Shimizu, I. (2020). Gait speed, life-space mobility and mild cognitive impairment in patients with coronary artery disease. Heart and Vessels, 0123456789. https://doi.org/10.1007/s00380-020-01677-yeng
dc.relation.referencesKaplan, K. A., Hirshman, J., Hernandez, B., Stefanick, M. L., Hoffman, A. R., Redline, S., Ancoli-Israel, S., Stone, K., Friedman, L., & Zeitzer, J. M. (2017). When a gold standard isn’t so golden: Lack of prediction of subjective sleep quality from sleep polysomnography. Biological Psychology, 123, 37–46. https://doi.org/10.1016/j.biopsycho.2016.11.010eng
dc.relation.referencesKarlsson, M. K., Magnusson, H., Von Schewelov, T., & Rosengren, B. E. (2013). Prevention of falls in the elderly - A review. Osteoporosis International, 24(3), 747–762. https://doi.org/10.1007/s00198-012-2256-7eng
dc.relation.referencesKassraian-Fard, P., Matthis, C., Balsters, J. H., Maathuis, M. H., & Wenderoth, N. (2016). Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example. Frontiers in Psychiatry, 7(DEC). https://doi.org/10.3389/fpsyt.2016.00177eng
dc.relation.referencesKennedy, R. E., Almutairi, M., Williams, C. P., Sawyer, P., Allman, R. M., & Brown, C. J. (2019). Determination of the Minimal Important Change in the Life-Space Assessment. Journal of the American Geriatrics Society, 67(3), 565–569. https://doi.org/10.1111/jgs.15707eng
dc.relation.referencesKennedy, R. E., Sawyer, P., Williams, C. P., Lo, A. X., Ritchie, C. S., Roth, D. L., Allman, R. M., & Brown, C. J. (2017). Life-Space Mobility Change Predicts 6-Month Mortality. Journal of the American Geriatrics Society, 65(4), 833–838. https://doi.org/10.1111/jgs.14738eng
dc.relation.referencesKennedy, R. E., Williams, C. P., Sawyer, P., Lo, A. X., Connelly, K., Nassel, A., & Brown, C. J. (2019). Life-Space Predicts Health Care Utilization in Community-Dwelling Older Adults. Journal of Aging and Health, 31(2), 280–292. https://doi.org/10.1177/0898264317730487eng
dc.relation.referencesKim, E., Choi, A., & Nam, H. (2019). Drug repositioning of herbal compounds via a machine-learning approach. BMC Bioinformatics, 20(Suppl 10).eng
dc.relation.referencesKim, H., Lee, K. M., Kim, E. J., & Lee, J. S. (2019). Improvement diagnostic accuracy of sinusitis recognition in paranasal sinus X-ray using multiple deep learning models. 9(6), 942–951. https://doi.org/10.21037/qims.2019.05.15eng
dc.relation.referencesKim, T. K., Yi, P. H., Wei, J., Shin, J. W., Hager, G., Hui, F. K., Sair, H. I., & Lin, C. T. (2019). Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs. Journal of Digital Imaging.eng
dc.relation.referencesKira, K., & Rendell, L. A. (1992). The Feature Selection Problem: Traditional Methods and a New Algorithm. AAAI-92 Proceedings. https://doi.org/10.1007/978-981-15-0512-6_5eng
dc.relation.referencesKirkman, M. S., Briscoe, V. J., Clark, N., Florez, H., Haas, L. B., Halter, J. B., Huang, E. S., Korytkowski, M. T., Munshi, M. N., Odegard, P. S., Pratley, R. E., & Swift, C. S. (2012). Diabetes in older adults. Diabetes Care, 35(12), 2650–2664. https://doi.org/10.2337/dc12-1801eng
dc.relation.referencesKononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 784 LNCS, 171–182.eng
dc.relation.referencesKruse, C., Goemaere, S., Lapauw, B., De Buyser, S., Eiken, P., & Vestergaard, P. (2018). Predicting mortality and incident immobility in older Belgian men by characteristics related to sarcopenia and frailty. Osteoporosis International, 29(6), 1437–1445. https://doi.org/10.1007/s00198-018-4467-zeng
dc.relation.referencesKuhn, M., & Johnson, K. (2019). Feature Engineering and Selection: A Practical Approach for Predictive Models. https://bookdown.org/max/FES/eng
dc.relation.referencesKumar, S. A., Yogesh, T., Prithiv, M., Alam, S. Q., Hashim, M. A. B., & Amutha, R. (2020). Data Mining Technique based Ambient Assisted Living for Elderly People. Proceedings of the 4th International Conference on Computing Methodologies and Communication, ICCMC 2020, Iccmc, 505–508. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00094eng
dc.relation.referencesKuspinar, A., Verschoor, C. P., Beauchamp, M. K., Dushoff, J., Ma, J., Amster, E., Bassim, C., Dal Bello-Haas, V., Gregory, M. A., Harris, J. E., Letts, L., Neil-Sztramko, S. E., Richardson, J., Valaitis, R., & Vrkljan, B. (2020). Modifiable factors related to life-space mobility in community-dwelling older adults: Results from the Canadian Longitudinal Study on Aging. BMC Geriatrics, 20(1). https://doi.org/10.1186/s12877-020-1431-5eng
dc.relation.referencesLaPatria. (2018). Caldas, segundo en envejecimiento en Colombia. https://www.lapatria.com/salud/caldas-segundo-en-envejecimiento-en-colombia-422635spa
dc.relation.referencesLee, H., Yune, S., Mansouri, M., Kim, M., Tajmir, S. H., Guerrier, C. E., Ebert, S. A., Pomerantz, S. R., Romero, J. M., Kamalian, S., Gonzalez, R. G., Lev, M. H., & Do, S. (2018). An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-018-0324-9eng
dc.relation.referencesLemaître, G., Nogueira, F., & West, W. S. (2017). Imbalanced-learn : A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. Journal of Machine Learning Research, 18, 1–5.eng
dc.relation.referencesLin, W. C., & Tsai, C. F. (2019). Missing value imputation : a review and analysis. Artificial Intelligence Review, 0123456789. https://doi.org/10.1007/s10462-019-09709-4eng
dc.relation.referencesLlano Escobar, R. (2019). Manizales, uno de los mejores ‘vivideros’ del país, según estudio. RCN Radio. https://www.rcnradio.com/colombia/eje-cafetero/manizales-es-el-mejor-vividero-del-pais-esto-dice-el-estudio-al-respectospa
dc.relation.referencesLondoño, E. (2020). En Manizales trabajan para evitar casos de suicidio. BCNoticias. https://www.bcnoticias.com.co/en-manizales-trabajan-para-evitar-casos-de-suicidio/spa
dc.relation.referencesLópez, G., Jerez, J., Franco, L., & Veredas, F. (2019). A Transfer-Learning Approach to Feature Extraction from Cancer Transcriptomes with Deep Autoencoders Guillermo. Springer Nature Switzerland, June, 283–296. https://doi.org/10.1007/978-3-030-20521-8eng
dc.relation.referencesLowe, A. (2019). Hyperparameters and Pipelines. Domino. https://blog.dominodatalab.com/towards-predictive-accuracy-tuning-hyperparameters-and-pipelines/eng
dc.relation.referencesMackey, D. C., Cauley, J. A., Barrett-Connor, E., Schousboe, J. T., Cawthon, P. M., & Cummings, S. R. (2014). Life-space mobility and mortality in older men: A prospective cohort study. Journal of the American Geriatrics Society, 62(7), 1288–1296. https://doi.org/10.1111/jgs.12892eng
dc.relation.referencesMata, G., Radojevi, M., Fernandez-lozano, C., Smal, I., Werij, N., Morales, M., Meijering, E., & Rubio, J. (2018). Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning. Neuroinformatics, Meijering 2010. https://doi.org/10.1007/s12021-018-9399-4eng
dc.relation.referencesMatsuda, K., Hamachi, N., Yamaguchi, T., Oka, S., Suzuki, A., Shimoda, T., Ikeda, T., Eguchi, M., Nakahara, M., Nagai, Y., Takano, Y., Kaneko, H., & Morita, M. (2019). A path analysis of the interdependent relationships between life space assessment scores and relevant factors in an elderly Japanese community. Journal of Physical Therapy Science, 31(4), 326–331. https://doi.org/10.1589/jpts.31.326eng
dc.relation.referencesMatsuda, K., Ikeda, S., Nakahara, M., Ikeda, T., Okamoto, R., Kurosawa, K., & Horikawa, E. (2015). Factors affecting the coefficient of variation of stride time of the elderly without falling history: a prospective study. Journal of Physical Therapy Science, 27(4), 1087–1090. https://doi.org/10.1589/jpts.27.1087eng
dc.relation.referencesMehta, S. D., & Sebro, R. (2020). Computer-Aided Detection of Incidental Lumbar Spine Fractures from Routine Dual-Energy X-Ray Absorptiometry (DEXA) Studies Using a Support Vector Machine (SVM) Classifier. Journal of Digital Imaging, 33(1), 204–210. https://doi.org/10.1007/s10278-019-00224-0eng
dc.relation.referencesMing, C., Viassolo, V., Probst-hensch, N., Chappuis, P. O., Dinov, I. D., & Katapodi, M. C. (2019). Machine learning techniques for personalized breast cancer risk prediction : comparison with the BCRAT and BOADICEA models. Breast Cancer Research, 1–11.eng
dc.relation.referencesMinisterio de Salud y Protección Social, O. de P. S. (2018). Sala situacional de la Población Adulta Mayor. Ministerio de Salud y Protección Social, 3–4. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/PS/sala-situacion-envejecimiento-2018.pdfspa
dc.relation.referencesMorton, N. A. De, Berlowitz, D. J., & Keating, J. L. (2008). A systematic review of mobility instruments and their measurement properties for older acute medical patients. 15(Mcid), 1–15. https://doi.org/10.1186/1477-7525-6-44eng
dc.relation.referencesMuñoz, N., Bosch, F. X., de Sanjosé, S., Herrero, R., Castellsagué, X., Shah, K. V., Snijders, P. J. F., & Meijer, C. J. L. M. (2003). Epidemiologic Classification of Human Papillomavirus Types Associated with Cervical Cancer. New England Journal of Medicine, 348(6), 518–527. https://doi.org/10.1056/nejmoa021641eng
dc.relation.referencesMurata, C., Kondo, T., Tamakoski, K., Yatsuya, H., & Toyoshima, H. (2006). Factors associated with life space among community-living rural elders in Japan. Public Health Nursing, 23(4), 324–331. https://doi.org/10.1111/j.1525-1446.2006.00568.xeng
dc.relation.referencesNikitha, A., & Sreeletha, S. . (2018). EMG based Gesture Recognition using Machine Learning. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Iciccs, 1560–1564.eng
dc.relation.referencesNorman, B., Pedoia, V., Noworolski, A., Link, T. M., & Majumdar, S. (2018). Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radiographs. Journal of Digital Imaging.eng
dc.relation.referencesNunes, N., Martins, B., Andr, N., Leite, F., & Silva, M. (2019). A Multi-modal Deep Learning Method for Classifying Chest Radiology Exams. 1, 323–335.eng
dc.relation.referencesONU. (2017). Envejecimiento. https://www.un.org/es/sections/issues-depth/ageing/index.htmlspa
dc.relation.referencesPang, S., Du, A., Orgun, M. A., & Yu, Z. (2018). A novel fused convolutional neural network for biomedical image classification. Medical & Biological Engineering & Computing.eng
dc.relation.referencesParvandeh, S., Yeh, H. W., Paulus, M. P., & McKinney, B. A. (2020). Consensus features nested cross-validation. Bioinformatics (Oxford, England), 36(10), 3093–3098. https://doi.org/10.1093/bioinformatics/btaa046eng
dc.relation.referencesPaterson, D. H., & Warburton, D. E. R. (2010). Physical activity and functional limitations in older adults: A systematic review related to Canada’s Physical Activity Guidelines. International Journal of Behavioral Nutrition and Physical Activity, 7. https://doi.org/10.1186/1479-5868-7-38eng
dc.relation.referencesPedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn : Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.eng
dc.relation.referencesPeel, C., Sawyer Baker, P., Roth, D. L., Brown, C. J., Brodner, E. V, & Allman, R. M. (2005). Assessing mobility in older adults: the UAB Study of Aging Life-Space Assessment. Physical Therapy, 85(10), 1008–1119. http://www.ncbi.nlm.nih.gov/pubmed/16180950eng
dc.relation.referencesPeña-Solano, D. M., Herazo-Dilson, M. I., & Calvo-Gómez, J. M. (2009). Depresión en ancianos. Revista de La Facultad de Medicina, 45(1), 347–355.spa
dc.relation.referencesPeña Q., É., & Curcio Borrero, C. L. (2016). Espacio de vida y entorno del barrio en adultos mayores de 65 a 74 años del área urbana de Manizales, Colombia. Revista Márgenes No, 13(19), 21–31. https://micologia.uv.cl/index.php/margenes/article/view/1031spa
dc.relation.referencesPeña Quimbaya, E. (2014). NIVEL DE ACTIVIDAD FÍSICA Y ESPACIO DE VIDA EN LOS ADULTOS MAYORES DE 65 A 74 AÑOS DEL ÁREA URBANA DE MANIZALES – COLOMBIA.spa
dc.relation.referencesPinder, L. (2016). Benefits Of Mobility In The Elderly. http://www.tribune242.com/news/2016/apr/06/benefits-mobility-elderly/eng
dc.relation.referencesPolku, H., Mikkola, T. M., Portegijs, E., Rantakokko, M., Kokko, K., Kauppinen, M., Rantanen, T., & Viljanen, A. (2015). Life-space mobility and dimensions of depressive symptoms among community-dwelling older adults. Aging & Mental Health, 19(9), 781–789. https://doi.org/10.1080/13607863.2014.977768eng
dc.relation.referencesPoranen-Clark, T., von Bonsdorff, M. B., Rantakokko, M., Portegijs, E., Eronen, J., Kauppinen, M., Eriksson, J. G., Rantanen, T., & Viljanen, A. (2017). Executive function and life-space mobility in old age. Aging Clinical and Experimental Research, 30(2), 145–151. https://doi.org/10.1007/s40520-017-0762-3eng
dc.relation.referencesPortegijs, E., Rantakokko, M., Mikkola, T. M., Viljanen, A., & Rantanen, T. (2014). Association between physical performance and sense of autonomy in outdoor activities and life-space mobility in community-dwelling older people. Journal of the American Geriatrics Society, 62(4), 615–621. https://doi.org/10.1111/jgs.12763eng
dc.relation.referencesPortegijs, E., Rantakokko, M., Viljanen, A., Sipilä, S., & Rantanen, T. (2016). Is frailty associated with life-space mobility and perceived autonomy in participation outdoors? A longitudinal study. Age and Ageing, 45(4), 550–553. https://doi.org/10.1093/ageing/afw072eng
dc.relation.referencesPortegijs, E., Tsai, L.-T., Rantanen, T., & Rantakokko, M. (2015). Moving through Life-Space Areas and Objectively Measured Physical Activity of Older People. PLOS ONE, 10(8), e0135308. https://doi.org/10.1371/journal.pone.0135308eng
dc.relation.referencesPugh, D. (2019). Balancing Datasets and Generating Synthetic Data with SMOTE. https://datasciencecampus.github.io/balancing-data-with-smote/eng
dc.relation.referencesRantakokko, M., Iwarsson, S., Slaug, B., & Nilsson, M. H. (2014). Life-space mobility in Parkinson’s disease: Associations with motor and non-motor symptoms. Rantakokko, M., Iwarsson, S., Slaug, B., & Nilsson, M. H. (2018). Life-Space Mobility in Parkinson’s Disease: Associations with Motor and Non-Motor Symptoms. The Journals of Gerontology: Series A. Doi:10.1093/Gerona/Gly074, 1–27. https://doi.org/10.1093/gerona/gly074/4965845eng
dc.relation.referencesRantakokko, M., Portegijs, E., Viljanen, A., Iwarsson, S., Kauppinen, M., & Rantanen, T. (2015). Changes in life-space mobility and quality of life among community-dwelling older people: a 2-year follow-up study. Quality of Life Research, 25(5), 1189–1197. https://doi.org/10.1007/s11136-015-1137-xeng
dc.relation.referencesRantakokko, M., Portegijs, E., Viljanen, A., Iwarsson, S., & Rantanen, T. (2017). Task Modifications in Walking Postpone Decline in Life-Space Mobility Among Community-Dwelling Older People: A 2-year Follow-up Study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 72(9), 1252–1256. https://doi.org/10.1093/gerona/glw348eng
dc.relation.referencesRantanen, T. (2013). Promoting mobility in older people. Journal of Preventive Medicine and Public Health, 46(SUPPL.1), 50–54. https://doi.org/10.3961/jpmph.2013.46.S.S50eng
dc.relation.referencesRazzak, M. I., Imran, M., & Xu, G. (2019). Big data analytics for preventive medicine. In Neural Computing and Applications (Vol. 0123456789). Springer London. https://doi.org/10.1007/s00521-019-04095-yeng
dc.relation.referencesRejeski, W. J., Ip, E. H., Marsh, A. P., & Barnard, R. T. (2010). Development and validation of a video-animated tool for assessing mobility. Journals of Gerontology - Series A Biological Sciences and Medical Sciences, 65 A(6), 664–671. https://doi.org/10.1093/gerona/glq055eng
dc.relation.referencesRemeseiro, B., & Bolon-Canedo, V. (2019). A review of feature selection methods in medical applications. Computers in Biology and Medicine, 112(February), 103375. https://doi.org/10.1016/j.compbiomed.2019.103375eng
dc.relation.referencesRitchie, C. S., Locher, J. L., Roth, D. L., McVie, T., Sawyer, P., & Allman, R. (2008). Unintentional weight loss predicts decline in activities of daily living function and life-space mobility over 4 years among community-dwelling older adults. Journals of Gerontology - Series A Biological Sciences and Medical Sciences, 63(1), 67–75. https://doi.org/10.1093/gerona/63.1.67eng
dc.relation.referencesRodrigo, J. A. (2017). Análisis de Componentes Principales (Principal Component Analysis, PCA) y t-SNE. https://www.cienciadedatos.net/documentos/35_principal_component_analysis#Ejemplo_cálculo_eigenvectors_y_eigenvaluesspa
dc.relation.referencesRollason, V., & Vogt, N. (2003). Reduction of polypharmacy in the elderly: A systematic review of the role of the pharmacist. Drugs and Aging, 20(11), 817–832. https://doi.org/10.2165/00002512-200320110-00003eng
dc.relation.referencesSaito, T., & Rehmsmeier, M. (2015). The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE, 1–21. https://doi.org/10.1371/journal.pone.0118432eng
dc.relation.referencesSánchez-Maroño, N., Bolón-Canedo, V., & Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data. 483–519. https://doi.org/10.1007/s10115-012-0487-8eng
dc.relation.referencesSanz, H., Valim, C., Vegas, E., Oller, J. M., & Reverter, F. (2018). SVM-RFE: Selection and visualization of the most relevant features through non-linear kernels. BMC Bioinformatics, 19(1), 1–18. https://doi.org/10.1186/s12859-018-2451-4eng
dc.relation.referencesSarmiento-Ramos, J. L. (2020). Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica. UIS Ingenierías, 19(4), 1–18.spa
dc.relation.referencesSatariano, W. A., Guralnik, J. M., Jackson, R. J., Marottoli, R. A., Phelan, E. A., & Prohaska, T. R. (2012). Mobility and aging: New directions for public health action. American Journal of Public Health, 102(8), 1508–1515. https://doi.org/10.2105/AJPH.2011.300631eng
dc.relation.referencesSawant, A., & N, N. K. (2019). Spark Machine Learning Pipelines to Predict Brain Tumor using Deep Learning. International Journal of Innovative Technology and Exploring Engineering, 7, 1444–1448.eng
dc.relation.referencesSawyer, P., & Allman, R. M. (2010). Resilience in mobility in the context of chronic disease and aging: Cross-sectional and prospective findings from the University of Alabama at Birmingham (UAB) study of aging. In New Frontiers in Resilient Aging: Life-Strengths and Well-Being in Late Life (pp. 310–339). https://doi.org/10.1017/CBO9780511763151.014eng
dc.relation.referencesSeger, C. (2018). An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing. Degree Project Technology, 41. http://www.diva-portal.org/smash/get/diva2:1259073/FULLTEXT01.pdfeng
dc.relation.referencesShabaniyan, T., Parsaei, H., Aminsharifi, A., & Mehdi, M. (2019). An artificial intelligence ‑ based clinical decision support system for large kidney stone treatment. Australasian Physical & Engineering Sciences in Medicine. https://doi.org/10.1007/s13246-019-00780-3eng
dc.relation.referencesShahbaz, M., & Ali, S. (2019). Classification of Alzheimer’s Disease using Machine Learning Techniques. 8th International Conference on Data Science, Technology and Applications, 2050.eng
dc.relation.referencesShalabi, L. Al, Shaaban, Z., & Kasasbeh, B. (2006). Data Mining: A Preprocessing Engine. Journal of Computer Science, 2(9), 735–739. https://doi.org/10.3844/jcssp.2006.735.739eng
dc.relation.referencesSheppard, K. D., Sawyer, P., Ritchie, C. S., Allman, R. M., & Brown, C. J. (2013). Life-space mobility predicts nursing home admission over 6 years. Journal of Aging and Health, 25(6), 907–920. https://doi.org/10.1177/0898264313497507eng
dc.relation.referencesShimada, H., Ishizaki, T., Kato, M., Morimoto, A., Tamate, A., Uchiyama, Y., & Yasumura, S. (2010). How often and how far do frail elderly people need to go outdoors to maintain functional capacity? Archives of Gerontology and Geriatrics, 50(2), 140–146. https://doi.org/10.1016/j.archger.2009.02.015eng
dc.relation.referencesShimada, H., Sawyer, P., Harada, K., Kaneya, S., Nihei, K., Asakawa, Y., Yoshii, C., Hagiwara, A., Furuna, T., & Ishizaki, T. (2010). Predictive Validity of the Classification Schema for Functional Mobility Tests in Instrumental Activities of Daily Living Decline Among Older Adults. Archives of Physical Medicine and Rehabilitation, 91(2), 241–246. https://doi.org/10.1016/j.apmr.2009.10.027eng
dc.relation.referencesShouman, M., Turner, T., & Stocker, R. (2011). Using Decision Tree for Diagnosing Heart Disease Patients. 23–29.eng
dc.relation.referencesSidey-Gibbons, J. A. M., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine : a practical introduction. BMCMedical Research Methodology, 4, 1–18.eng
dc.relation.referencesSiltanen, S., Rantanen, T., Portegijs, E., Tourunen, A., Poranen-Clark, T., Eronen, J., & Saajanaho, M. (2019). Association of tenacious goal pursuit and flexible goal adjustment with out-of-home mobility among community-dwelling older people. Aging Clinical and Experimental Research, 31(9), 1249–1256. https://doi.org/10.1007/s40520-018-1074-yeng
dc.relation.referencesSilva da Sá, J. A., Almeida, A. C., Rocha, B. R. P., Mota, M. A. S., Souza, J. R. S., & Dentel, L. M. (2016). Lightning Forecast Using Data Mining Techniques On Hourly Evolution Of The Convective Available Potential Energy. 10th Brazilian Congress on Computational Intelligence, August, 1–5. https://doi.org/10.21528/cbic2011-27.1eng
dc.relation.referencesSimões, M. do S. M. P., Garcia, I. F. F., Costa, L. da C. M., & Lunardi, A. C. (2018). Life-Space Assessment questionnaire: Novel measurement properties for Brazilian community-dwelling older adults. Geriatrics and Gerontology International, 18(5), 783–789. https://doi.org/10.1111/ggi.13263eng
dc.relation.referencesSmith, A. R., Chen, C., Clarke, P., & Gallagher, N. A. (2016). Trajectories of Outdoor Mobility in Vulnerable Community-Dwelling Elderly: The Role of Individual and Environmental Factors. Journal of Aging and Health, 28(5), 796–811. https://doi.org/10.1177/0898264315611665eng
dc.relation.referencesSnih, S. Al, Peek, K. M., Sawyer, P., Markides, K. S., Allman, R. M., & Ottenbacher, K. J. (2012). Life-space mobility in Mexican Americans aged 75 and older. Journal of the American Geriatrics Society. https://doi.org/10.1111/j.1532-5415.2011.03822.xeng
dc.relation.referencesSong, Y. Y., & Lu, Y. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi.org/10.11919/j.issn.1002-0829.215044eng
dc.relation.referencesSoni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. International Journal of Computer Applications (0975 – 8887), 17(8), 119–138. https://doi.org/10.4337/9781848442986.00014eng
dc.relation.referencesSun, Y., Shi, H., Zhang, S., & Wang, P. (2019). Accurate and rapid CT image segmentation of the eyes and surrounding organs for precise radiotherapy. 2214–2222. https://doi.org/10.1002/mp.13463eng
dc.relation.referencesSuzuki, T., Kitaike, T., & Ikezaki, S. (2014). Life-space mobility and social support in elderly adults with orthopaedic disorders. International Journal of Nursing Practice, 20(S1), 32–38. https://doi.org/10.1111/ijn.12248eng
dc.relation.referencesSwindell, W. R., Cummings, S. R., Sanders, J. L., Caserotti, P., Rosano, C., Satterfield, S., Strotmeyer, E. S., Harris, T. B., Simonsick, E. M., & Cawthon, P. M. (2012). Data Mining Identifies Digit Symbol Substitution Test Score and Serum Cystatin C as Dominant Predictors of Mortality in Older Men and Women. Rejuvenation Research, 15(4), 405–413. https://doi.org/10.1089/rej.2011.1297eng
dc.relation.referencesTaylor, J. K., Buchan, I. E., & van der Veer, S. N. (2018). Assessing life-space mobility for a more holistic view on wellbeing in geriatric research and clinical practice. Aging Clinical and Experimental Research. https://doi.org/10.1007/s40520-018-0999-5eng
dc.relation.referencesTombaugh, T. N., & McIntyre, N. (1992). Relationship between Areas of Cognitive Functioning on the Mini-Mental State Examination and Crash Risk. Geriatrics, 3(1), 10. https://doi.org/10.3390/geriatrics3010010eng
dc.relation.referencesTsai, L. T., Portegijs, E., Rantakokko, M., Viljanen, A., Saajanaho, M., Eronen, J., & Rantanen, T. (2015). The association between objectively measured physical activity and life-space mobility among older people. Scandinavian Journal of Medicine and Science in Sports, 25(4), e368–e373. https://doi.org/10.1111/sms.12337eng
dc.relation.referencesTsai, Li Tang, Rantakokko, M., Rantanen, T., Viljanen, A., Kauppinen, M., & Portegijs, E. (2016). Objectively Measured Physical Activity and Changes in Life-Space Mobility among Older People. Journals of Gerontology - Series A Biological Sciences and Medical Sciences, 71(11), 1466–1471. https://doi.org/10.1093/gerona/glw042eng
dc.relation.referencesTsai, Li Tang, Rantakokko, M., Viljanen, A., Saajanaho, M., Eronen, J., Rantanen, T., & Portegijs, E. (2016). Associations between reasons to go outdoors and objectively-measured walking activity in various life-space areas among older people. Journal of Aging and Physical Activity, 24(1), 85–91. https://doi.org/10.1123/japa.2014-0292eng
dc.relation.referencesTseng, Y. C., Gau, B. S., & Lou, M. F. (2020). Validation of the Chinese version of the Life-Space Assessment in community-dwelling older adults. Geriatric Nursing, 41(4), 381–386. https://doi.org/10.1016/j.gerinurse.2019.11.014eng
dc.relation.referencesTuruba, R., Pirkle, C., Bélanger, E., Ylli, A., Gomez Montes, F., & Vafaei, A. (2020). Assessing the relationship between multimorbidity and depression in older men and women: the International Mobility in Aging Study (IMIAS). Aging and Mental Health, 24(5), 747–757. https://doi.org/10.1080/13607863.2019.1571018eng
dc.relation.referencesUhm, K. E., Oh-Park, M., Kim, Y.-S., Park, J.-M., Cho, J., Moon, Y., Han, S.-H., Hwan, J. H., Lee, K. S., & Lee, J. (2020). Applicability of the 48/6 Model of Care as a Health Screening Tool, and its Association with Mobility in Community-Dwelling Older Adults. Journal of Korean Medical Science, 35(34), e308. https://doi.org/10.3346/jkms.2020.35.e308eng
dc.relation.referencesUrbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics, 85(June), 189–203. https://doi.org/10.1016/j.jbi.2018.07.014eng
dc.relation.referencesUrda, D., Veredas, F. J., Turias, I., & Franco, L. (2019). Addition of Pathway-Based Information to Improve Predictions in Transcriptomics. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11466 LNBI, 200–208. https://doi.org/10.1007/978-3-030-17935-9_19eng
dc.relation.referencesVeloso, F. (2019). Random Forest en Regresión Para Machine Learning. Feedingthemachine. https://www.feedingthemachine.cl/random-forest-en-regresion-para-machine-learning/eng
dc.relation.referencesViljanen, A., Mikkola, T. M., Rantakokko, M., Portegijs, E., & Rantanen, T. (2016). The Association between Transportation and Life-Space Mobility in Community-Dwelling Older People with or Without Walking Difficulties. Journal of Aging and Health, 28(6), 1038–1054. https://doi.org/10.1177/0898264315618919eng
dc.relation.referencesWainer, J., & Cawley, G. (2018). Nested cross-validation when selecting classifiers is overzealous for most practical applications. Journal of Machine Learning Research, September. http://arxiv.org/abs/1809.09446eng
dc.relation.referencesWebber, S. C., Porter, M. M., & Menec, V. H. (2010). Mobility in older adults: A comprehensive framework. Gerontologist, 50(4), 443–450. https://doi.org/10.1093/geront/gnq013eng
dc.relation.referencesWells, E. U., Williams, C. P., Kennedy, R. E., Sawyer, P., & Brown, C. J. (2020). Factors That Contribute to Recovery of Community Mobility After Hospitalization Among Community-Dwelling Older Adults. Journal of Applied Gerontology, 39(4), 435–441. https://doi.org/10.1177/0733464818770788eng
dc.relation.referencesWillis, A. W. (2013). Parkinson disease in the elderly adult. Missouri Medicine, 110(5), 406–410.eng
dc.relation.referencesWu, L., Liu, Z., Bera, T., Ding, H., & Langley, D. A. (2019). A deep learning model to recognize food contaminating beetle species based on elytra fragments. Computers and Electronics in Agriculture, 166(September), 105002. https://doi.org/10.1016/j.compag.2019.105002eng
dc.relation.referencesWu, Z., Wang, X., & Jiang, B. (2020). Fault diagnosis for wind turbines based on ReliefF and eXtreme gradient boosting. Applied Sciences (Switzerland), 10(9). https://doi.org/10.3390/app10093258eng
dc.relation.referencesXu, J., Zheng, X., & Jiang, M. (2019). Gene Mutation Classification Using CNN and BiGRU Network. 2019 9th International Conference on Information Science and Technology (ICIST), 397–401.eng
dc.relation.referencesXue, Q. L., Fried, L. P., Glass, T. A., Laffan, A., & Chaves, P. H. M. M. (2008). Life-space constriction, development of frailty, and the competing risk of mortality: The women’s health and aging study I. American Journal of Epidemiology, 167(2), 240–248. https://doi.org/10.1093/aje/kwm270eng
dc.relation.referencesYang, H., & Bath, P. A. (2020). The Use of Data Mining Methods for the Prediction of Dementia: Evidence from the English Longitudinal Study of Aging. IEEE Journal of Biomedical and Health Informatics, 24(2), 345–353. https://doi.org/10.1109/JBHI.2019.2921418eng
dc.relation.referencesYoon, S., Suero-Tejeda, N., & Bakken, S. (2015). A Data Mining Approach for Examining Predictors of Physical Activity among Older Urban Adults. Physiology & Behavior, 41(7), 14–20. https://doi.org/10.1016/j.physbeh.2017.03.040eng
dc.relation.referencesZhang, J., Wang, S., Chen, L., Guo, G., Chen, R., & Vanasse, A. (2019). Time-Dependent Survival Neural Network for Remaining Useful Life Prediction. In Advances in Knowledge Discovery and Data Mining. PAKDD 2019. (Vol. 11439). Springer International Publishing. https://doi.org/10.1007/978-3-030-16148-4eng
dc.relation.referencesZhang, W., & Li, Y. Y. J. (2019). Dynamics reconstruction and classification via Koopman features. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-019-00639-xeng
dc.relation.referencesZheng, S., Wang, Y., Liu, H., Chang, W., Xu, Y., & Lin, F. (2019). Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods [Research-article]. Chemical Research in Toxicology, 32, 1014–1026.eng
dc.relation.referencesZoltan, C. (2018). SVM and Kernel SVM. https://towardsdatascience.com/svm-and-kernel-svm-fed02bef1200eng
dc.relation.referencesZukotynski, K., Gaudet, V. C., Kuo, P., Adamo, S., Goubran, M., Bocti, C., Borrie, M., Frayne, R., Hsiung, R., Laforce, R. J., Noseworthy, M. D., Prato, F. S., & Sahlas, J. D. (2019). Non-Binary Approaches for Classification of Amyloid Brain PET. 2019 IEEE 49th International Symposium on Multiple-Valued Logic (ISMVL), 206–211. https://doi.org/10.1109/ISMVL.2019.00043eng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónspa
dc.subject.lembMinería de datos -- Adulto mayor – Metodologíaspa
dc.subject.lembData mining -- Elderly -- Methodologyeng
dc.subject.proposalMovilidadspa
dc.subject.proposalRestricciónspa
dc.subject.proposalMinería de datosspa
dc.subject.proposalAdultos mayoresspa
dc.subject.proposalAlgoritmospa
dc.subject.proposalMobilityeng
dc.subject.proposalRestrictioneng
dc.subject.proposalData miningeng
dc.subject.proposalElderlyeng
dc.subject.proposalAlgorithmeng
dc.titleTécnicas de minería de datos supervisadas en el espacio de vida de adultos mayores de la ciudad de Manizalesspa
dc.title.translatedSupervised techniques data mining in life space of Manizales elderly peopleeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentImagespa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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