Ciencia de datos e inteligencia artificial en la predicción de eventos cardiovasculares en pacientes hipertensos: una revisión integrativa de la literatura

Núñez Niño, Daniel Enrique

Ciencia de datos e inteligencia artificial en la predicción de eventos cardiovasculares en pacientes hipertensos: una revisión integrativa de la literatura

dc.contributor.advisor	Suárez Rojas, Angie Lorena
dc.contributor.author	Núñez Niño, Daniel Enrique
dc.contributor.corporatename	Universidad Santo Tomás
dc.contributor.cvlac	https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001829344
dc.date.accessioned	2026-07-28T21:24:22Z
dc.date.available	2026-07-28T21:24:22Z
dc.date.issued	2026-07-10
dc.description	Las enfermedades del sistema cardiovascular son la principal causa de mortalidad en el mundo. La hipertensión arterial es el principal factor de riesgo cardiovascular que puede evitarse. En disminuir las muertes por estas enfermedades, el uso de tecnologías innovadoras en esta región es de gran importancia. Este artículo de divulgación se enfatiza en los datos de la región de Tunja, ciudad que ha sido considerado el área de salud en datos, y datos de enfermedades de Tunja, y por tanto, se presentan las previsiones del uso de Ciencia de Datos y de la Inteligencia Artificial en el análisis de la predicción de eventos cardiovasculares en pacientes hipertensos. La revisión de estudios y de la literatura que han sido publicados en bases de datos, y que en la actualidad generan gran impacto en el área de tecnologías de la salud, ha sido sistemática, y los estudios han sido analizados de manera critica, en el uso de herramientas de aprendizaje automático y de las redes neuronales. La aplicación realizada demostró que modelos como Random Forest, XGBoost, y redes neuronales cilíndricas de memoria a largo y corto plazo, presentaron más del 88% de eficiencia en la predicción de infarto agudo de miocardio, en evento v y/o muerte cardiovascular en empresas de hipertensión. La incorporación de herramientas de ciencia de datos en sistemas de salud abiertos proveer conciencia sobre los recursos de prevención y disminución a lo esencial de la medicina.
dc.description.abstract	Diseases of the cardiovascular system are the leading cause of mortality in the world. Arterial hypertension is the main cardiovascular risk factor that can be avoided. In reducing deaths from these diseases, the use of innovative technologies in this region is of great importance. This popular article emphasizes the data of the Tunja region, a municipality that has been considered the health area in data, and disease data of Tunja, and therefore, the forecasts of the use of Data Science and Artificial Intelligence in the analysis of the prediction of cardiovascular events in hypertensive patients are presented. The review of studies and literature that have been published in databases, and that currently generate great impact in the area of health technologies, has been systematic, and the studies have been critically analyzed, in the use of machine learning tools and neural networks. The application showed that models such as Random Forest, XGBoost, and cylindrical neural networks of long-term and short-term memory, presented more than 88% efficiency in the prediction of acute myocardial infarction, event v and/or cardiovascular death in hypertension companies. The incorporation of data science tools in open health systems provide awareness about prevention resources and reduce the essentials of medicine.
dc.description.degreelevel	Pregrado	spa
dc.description.degreename	Ingeniero Informático	spa
dc.description.domain	http://www.ustatunja.edu.co/investigacion
dc.format.mimetype	application/pdf
dc.identifier.citation	Nuñez Niño, D. E. (2026). Ciencia de datos e inteligencia artificial en la predicción de eventos cardiovasculares en pacientes hipertensos: una revisión integrativa de la literatura [Trabajo de Grado, Universidad Santo Tomás].Repositorio Institucional
dc.identifier.instname	instname:Universidad Santo Tomás	spa
dc.identifier.reponame	reponame:Repositorio Institucional Universidad Santo Tomás	spa
dc.identifier.repourl	repourl:https://repository.usta.edu.co	spa
dc.identifier.uri	http://hdl.handle.net/11634/73610
dc.language.iso	spa
dc.publisher	Universidad Santo Tomás	spa
dc.publisher.branch	CRAI-USTA Tunja
dc.publisher.faculty	Facultad de Ingeniería de Sistemas	spa
dc.publisher.program	Ingeniería Informática	spa
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dc.rights	Attribution-NonCommercial-NoDerivs 2.5 Colombia	en
dc.rights.accessrights	info:eu-repo/semantics/openAccess
dc.rights.coar	http://purl.org/coar/access_right/c_abf2
dc.rights.local	Abierto (Texto Completo)	spa
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keyword	Arterial hypertension
dc.subject.keyword	Data science
dc.subject.keyword	Artificial intelligence
dc.subject.keyword	Cardiovascular prediction
dc.subject.keyword	Machine learning
dc.subject.keyword	Cardiovascular events
dc.subject.keyword	Public health
dc.subject.proposal	Hipertensión arterial
dc.subject.proposal	Ciencia de datos
dc.subject.proposal	Inteligencia artificial
dc.subject.proposal	Predicción cardiovascular
dc.subject.proposal	Aprendizaje automático
dc.subject.proposal	Eventos cardiovasculares
dc.subject.proposal	Salud pública
dc.title	Ciencia de datos e inteligencia artificial en la predicción de eventos cardiovasculares en pacientes hipertensos: una revisión integrativa de la literatura
dc.type	bachelor thesis
dc.type.coar	http://purl.org/coar/resource_type/c_7a1f
dc.type.coarversion	http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.drive	info:eu-repo/semantics/bachelorThesis
dc.type.local	Trabajo de grado	spa
dc.type.version	info:eu-repo/semantics/acceptedVersion