Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático

Diabetes in Colombia is one of the leading causes of death in most of the country's departments, according to the Ministry of Health. The World Health Organization recognizes three main types of diabetes: type I, type II, and gestational. One of the main causes of death from diabetes is that wh...

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Autores:
Pérez Leal, Leydi Esperanza
Buitrago C´ardenas, José Alejandro
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2021
Institución:
Universidad Antonio Nariño
Repositorio:
Repositorio UAN
Idioma:
spa
OAI Identifier:
oai:repositorio.uan.edu.co:123456789/4816
Acceso en línea:
http://repositorio.uan.edu.co/handle/123456789/4816
Palabra clave:
Aprendizaje Automático
Diabetes
004
616
Machine learning
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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dc.title.es_ES.fl_str_mv Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
title Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
spellingShingle Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
Aprendizaje Automático
Diabetes
004
616
Machine learning
title_short Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
title_full Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
title_fullStr Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
title_full_unstemmed Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
title_sort Predicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automático
dc.creator.fl_str_mv Pérez Leal, Leydi Esperanza
Buitrago C´ardenas, José Alejandro
dc.contributor.advisor.spa.fl_str_mv Ramírez, Juan Camilo
dc.contributor.author.spa.fl_str_mv Pérez Leal, Leydi Esperanza
Buitrago C´ardenas, José Alejandro
dc.subject.es_ES.fl_str_mv Aprendizaje Automático
Diabetes
topic Aprendizaje Automático
Diabetes
004
616
Machine learning
dc.subject.ddc.es_ES.fl_str_mv 004
616
dc.subject.keyword.es_ES.fl_str_mv Machine learning
description Diabetes in Colombia is one of the leading causes of death in most of the country's departments, according to the Ministry of Health. The World Health Organization recognizes three main types of diabetes: type I, type II, and gestational. One of the main causes of death from diabetes is that when the patient is diagnosed, the disease is already advanced and therefore difficult to treat. Therefore, it is very important to make a diagnosis in time, so that the factors that derive from this event can be minimized, such as: serious complications (such as: amputations, heart attacks, eye damage, foot ulcer, among others.); monetary expenses (such as: hospital, personal, state); time invested, among others. One of the methods used and making use of technology is the prediction of the risk of developing diabetes using machine learning (ML), where the prognosis of the disease is obtained as a result and with it, prevention fatal results and reduction of financial expenses. This process has already been carried out over time and there are several studies in which an attempt is made to predict the diagnosis of diabetes using machine learning.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-09-03T21:02:23Z
dc.date.available.none.fl_str_mv 2021-09-03T21:02:23Z
dc.date.issued.spa.fl_str_mv 2021-05-27
dc.type.spa.fl_str_mv Trabajo de grado (Pregrado y/o Especialización)
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dc.identifier.bibliographicCitation.spa.fl_str_mv [1] M. M. F. Islam, R. Ferdousi, S. Rahman, and H. Y. Bushra, “Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques,” Adv. Intell. Syst. Comput., vol. 992, pp. 113-125, 2020, doi: 10.1007/978-981-13-8798-2-12
2] B. J. Lee and J. Y. Kim, “Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on Machine Learning,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 1, pp. 39–46, Jan. 2016, doi: 10.1109/JBHI.2015.2396520.
[3] B. J. Lee and J. Y. Kim, “Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on Machine Learning,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 1, pp. 39–46, Jan. 2016, doi: 10.1109/JBHI.2015.2396520
[4] SANCHEZ RIVERO, Germ´an. Historia de la diabetes. Gac Med Bol ´ [online]. 2007, vol.30, n.2 [citado 2021-02-15], pp. 74-78 . Disponible en: http://www.scielo.org.bo/scielo.php?script=sci- arttext y pid=S1012- 29662007000200016&lng=es&nrm=iso¿. ISSN 1012-2966.
[5] Villalobos A, Rojas-Mart´ınez R, Aguilar-Salinas CA, et al. Atenci´on m´edica y acciones de autocuidado en personas que viven con diabetes, seg´un nivel socioecon´omico. salud p´ublica mex. 2019;61(6):876-887.
[6] G´omez-Encino, Guadalupe del Carmen, Cruz-Le´on, Aralucy, Zapata-V´azquez, Rosario, Morales- Ram´on, Fabiola Nivel de conocimiento que tienen los pacientes con Diabetes Mellitus tipo 2 en relaci´on a su enfermedad. Salud en Tabasco [en linea]. 2015, 21(1), 17-25[fecha de Consulta 15 de Febrero de 2021]. ISSN: 1405-2091. Disponible en: https://www.redalyc.org/articulo.oa?id=48742127004
[7] D. I. Conget, “Diagnosis, classification and pathogenesis of diabetes mellitus,” Rev. Esp. Cardiol., vol. 55, no. 5, pp. 528–535, Jan. 2002, doi: 10.1016/S0300-8932(02)76646-3
[8] AMERICAN DIABETES ASSOCIATION, “Diagnosis and Classification of Diabetes Mellitus,” 2005.
[9] World Health Organization, “OMS — Diabetes,” 2020. https://www.who.int/diabetes/action-online/basics/es/index3.html (accessed Sep. 06, 2020).
[10] A. D. Association, “Classification and diagnosis of diabetes,” Diabetes Care, vol. 40, no. Supplement 1, pp. S11–S24, Jan. 2017, doi: 10.2337/dc17-S005.
dc.identifier.instname.spa.fl_str_mv instname:Universidad Antonio Nariño
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url http://repositorio.uan.edu.co/handle/123456789/4816
identifier_str_mv [1] M. M. F. Islam, R. Ferdousi, S. Rahman, and H. Y. Bushra, “Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques,” Adv. Intell. Syst. Comput., vol. 992, pp. 113-125, 2020, doi: 10.1007/978-981-13-8798-2-12
2] B. J. Lee and J. Y. Kim, “Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on Machine Learning,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 1, pp. 39–46, Jan. 2016, doi: 10.1109/JBHI.2015.2396520.
[3] B. J. Lee and J. Y. Kim, “Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on Machine Learning,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 1, pp. 39–46, Jan. 2016, doi: 10.1109/JBHI.2015.2396520
[4] SANCHEZ RIVERO, Germ´an. Historia de la diabetes. Gac Med Bol ´ [online]. 2007, vol.30, n.2 [citado 2021-02-15], pp. 74-78 . Disponible en: http://www.scielo.org.bo/scielo.php?script=sci- arttext y pid=S1012- 29662007000200016&lng=es&nrm=iso¿. ISSN 1012-2966.
[5] Villalobos A, Rojas-Mart´ınez R, Aguilar-Salinas CA, et al. Atenci´on m´edica y acciones de autocuidado en personas que viven con diabetes, seg´un nivel socioecon´omico. salud p´ublica mex. 2019;61(6):876-887.
[6] G´omez-Encino, Guadalupe del Carmen, Cruz-Le´on, Aralucy, Zapata-V´azquez, Rosario, Morales- Ram´on, Fabiola Nivel de conocimiento que tienen los pacientes con Diabetes Mellitus tipo 2 en relaci´on a su enfermedad. Salud en Tabasco [en linea]. 2015, 21(1), 17-25[fecha de Consulta 15 de Febrero de 2021]. ISSN: 1405-2091. Disponible en: https://www.redalyc.org/articulo.oa?id=48742127004
[7] D. I. Conget, “Diagnosis, classification and pathogenesis of diabetes mellitus,” Rev. Esp. Cardiol., vol. 55, no. 5, pp. 528–535, Jan. 2002, doi: 10.1016/S0300-8932(02)76646-3
[8] AMERICAN DIABETES ASSOCIATION, “Diagnosis and Classification of Diabetes Mellitus,” 2005.
[9] World Health Organization, “OMS — Diabetes,” 2020. https://www.who.int/diabetes/action-online/basics/es/index3.html (accessed Sep. 06, 2020).
[10] A. D. Association, “Classification and diagnosis of diabetes,” Diabetes Care, vol. 40, no. Supplement 1, pp. S11–S24, Jan. 2017, doi: 10.2337/dc17-S005.
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dc.publisher.program.spa.fl_str_mv Ingeniería de Sistemas (Distancia)
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería de Sistemas
dc.publisher.campus.spa.fl_str_mv Bogotá - Sur
institution Universidad Antonio Nariño
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spelling Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ramírez, Juan CamiloPérez Leal, Leydi EsperanzaBuitrago C´ardenas, José Alejandro11161614475111616152862021-09-03T21:02:23Z2021-09-03T21:02:23Z2021-05-27http://repositorio.uan.edu.co/handle/123456789/4816[1] M. M. F. Islam, R. Ferdousi, S. Rahman, and H. Y. Bushra, “Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques,” Adv. Intell. Syst. Comput., vol. 992, pp. 113-125, 2020, doi: 10.1007/978-981-13-8798-2-122] B. J. Lee and J. Y. Kim, “Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on Machine Learning,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 1, pp. 39–46, Jan. 2016, doi: 10.1109/JBHI.2015.2396520.[3] B. J. Lee and J. Y. Kim, “Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on Machine Learning,” IEEE J. Biomed. Heal. Informatics, vol. 20, no. 1, pp. 39–46, Jan. 2016, doi: 10.1109/JBHI.2015.2396520[4] SANCHEZ RIVERO, Germ´an. Historia de la diabetes. Gac Med Bol ´ [online]. 2007, vol.30, n.2 [citado 2021-02-15], pp. 74-78 . Disponible en: http://www.scielo.org.bo/scielo.php?script=sci- arttext y pid=S1012- 29662007000200016&lng=es&nrm=iso¿. ISSN 1012-2966.[5] Villalobos A, Rojas-Mart´ınez R, Aguilar-Salinas CA, et al. Atenci´on m´edica y acciones de autocuidado en personas que viven con diabetes, seg´un nivel socioecon´omico. salud p´ublica mex. 2019;61(6):876-887.[6] G´omez-Encino, Guadalupe del Carmen, Cruz-Le´on, Aralucy, Zapata-V´azquez, Rosario, Morales- Ram´on, Fabiola Nivel de conocimiento que tienen los pacientes con Diabetes Mellitus tipo 2 en relaci´on a su enfermedad. Salud en Tabasco [en linea]. 2015, 21(1), 17-25[fecha de Consulta 15 de Febrero de 2021]. ISSN: 1405-2091. Disponible en: https://www.redalyc.org/articulo.oa?id=48742127004[7] D. I. Conget, “Diagnosis, classification and pathogenesis of diabetes mellitus,” Rev. Esp. Cardiol., vol. 55, no. 5, pp. 528–535, Jan. 2002, doi: 10.1016/S0300-8932(02)76646-3[8] AMERICAN DIABETES ASSOCIATION, “Diagnosis and Classification of Diabetes Mellitus,” 2005.[9] World Health Organization, “OMS — Diabetes,” 2020. https://www.who.int/diabetes/action-online/basics/es/index3.html (accessed Sep. 06, 2020).[10] A. D. Association, “Classification and diagnosis of diabetes,” Diabetes Care, vol. 40, no. Supplement 1, pp. S11–S24, Jan. 2017, doi: 10.2337/dc17-S005.instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/Diabetes in Colombia is one of the leading causes of death in most of the country's departments, according to the Ministry of Health. The World Health Organization recognizes three main types of diabetes: type I, type II, and gestational. One of the main causes of death from diabetes is that when the patient is diagnosed, the disease is already advanced and therefore difficult to treat. Therefore, it is very important to make a diagnosis in time, so that the factors that derive from this event can be minimized, such as: serious complications (such as: amputations, heart attacks, eye damage, foot ulcer, among others.); monetary expenses (such as: hospital, personal, state); time invested, among others. One of the methods used and making use of technology is the prediction of the risk of developing diabetes using machine learning (ML), where the prognosis of the disease is obtained as a result and with it, prevention fatal results and reduction of financial expenses. This process has already been carried out over time and there are several studies in which an attempt is made to predict the diagnosis of diabetes using machine learning.La diabetes en Colombia es una de las principales causas de muerte en la mayoría de los departamentos del país, según el Ministerio de Salud. La Organización Mundial de la Salud reconoce tres tipos principales de diabetes: tipo I, tipo II y gestacional. Una de las principales causas de mortandad por diabetes es que cuando el paciente es diagnosticado, la enfermedad ya esta avanzada y por ende es difícil de tratar. Por lo tanto, es de gran importancia realizar un diagnostico a tiempo, para que se puedan minimizar los factores que se derivan de este acontecimiento, como lo son: complicaciones graves (como: amputaciones, ataques cardiacos, daño ocular, ´ulcera en el pie, entre otros.); gastos monetarios (como: hospitalarios, personales, del estado); tiempo invertido, entre otros. Uno de los métodos empleados y haciendo uso de la tecnología, es la predicción del riesgo de desarrollar diabetes usando machine learning (ML), en donde se obtiene como resultado el pronostico de la enfermedad y con ello, prevenir los resultados fatales y reducción de gastos financieros. Este proceso ya se ha venido realizando con el paso del tiempo y se encuentran varios estudios en donde se intenta predecir el diagnostico de la diabetes utilizando aprendizaje automáticoIngeniero(a) de Sistemas (Distancia)PregradoPresencialMonografíaspaUniversidad Antonio NariñoIngeniería de Sistemas (Distancia)Facultad de Ingeniería de SistemasBogotá - SurAprendizaje AutomáticoDiabetes004616Machine learningPredicción del diagnostico de diabetes a partir de perfiles clínicos de pacientes utilizando aprendizaje automáticoTrabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85EspecializadaORIGINAL2021_LeidyEsperanzaPerezLeal_Acta.pdf2021_LeidyEsperanzaPerezLeal_Acta.pdfActa de 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