Data analysis model to categorize the level of cardiovascular risk in the Department of Atlantico-Colombia

Paul Sanmartin; Luis Consuegra; Diana Heredia; Monica Huerta

doi:10.32397/tesea.vol6.n2.828

Authors

Paul Sanmartin Universidad Simón Bolívar , Simón Bolívar University , Simón Bolívar University
Luis Consuegra Universidad Simón Bolívar , Simón Bolívar University , Simón Bolívar University https://orcid.org/0000-0002-1747-1963
Diana Heredia Universidad Simón Bolívar , Simón Bolívar University , Simón Bolívar University
Monica Huerta State University of Milagro

DOI:

https://doi.org/10.32397/tesea.vol6.n2.828

Keywords:

Cardiovascular disease, cardiovascular risk factors, cardiovascular prediction models

Abstract

This article proposes a model based on data analysis to categorize the level of cardiovascular risk in department of Atlantico-Colombia. In the process, the fundamental concepts on which this research proposal was based were identified. As a result, different risk estimation methods were identified according to the region where the studies were carried out; therefore, it was necessary to determine the most appropriate method for the target population of the proposed model, determining a cardiovascular risk prediction model validated in Colombia. To develop the model, we took into account the basis of the concepts and previous studies on cardiovascular risk estimation, identifying during the process the elements involved in the data analysis process, determining the components of the model and characterizing the functional relationships between them.

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References

World Health Organization. The top 10 causes of death, 2018. Retrieved from http://www.who.int/en/news-room/factsheets/detail/the-top-10-causes-of-death.

Ministerio de salud. Principales causas de mortalidad en colombia, 2010. Retrieved from https://www.minsalud.gov.co/ salud/Paginas/Enfermedades-cardiovasculares.aspx.

José Canchila-Agámez, Santiago García-Cardona, Cesar Viloria-Nuñez, Jose Atilio Nuñez-Ramos, and David Rivas-Lalaleo. Proposed architecture for a patient management and classification system in the triage process. In International Conference on Technological Innovation and AI Research (ICTIAIR 2025), volume 2025, pages 127–132. IET, 2025.

M. S. Campos de Aldana, D. Moya Plata, J. D. Mendoza Matajira, and E. Y. Duran Niño. Chronic non-communicable diseases and use of information and communication technology: Systematic review. Revista Cuidarte, 5(1):661–669, 2014.

World Health Organization. Enfermedades no transmisibles, 2018. Retrieved from https://www.who.int/es/news-room/factsheets/detail/noncommunicable-diseases.

World Health Organization. Global status report on noncommunicable diseases 2014, 2014. Retrieved from https: //www.who.int/nmh/publications/ncd-status-report-2014/en/.

J. E. Jiménez Mesino. Autopercepción del estado de salud en individuos con riesgo cardiovascular en Colombia. PhD thesis, Master’s thesis, 2019. Universidad del Norte.

A. Álvarez Cosmea. Las tablas de riesgo cardiovascular: Una revisión crítica. Medifam, 11(3):20–51, 2001. Recuperado en 06 de enero de 2025.

B. A. Goldstein, A. M. Navar, and M. J. Pencina. Risk prediction with electronic health records: the importance of model validation and clinical context. JAMA cardiology, 1(9):976–977, 2016.

Yasuhiro Mukai, Koki Nakanishi, Masao Daimon, Kentaro Iwama, Yuriko Yoshida, Kazutoshi Hirose, Yuko Yamamoto, Hikari Seki, Tomoko Nakao, Tsukasa Oshima, Takumi Matsubara, Yu Shimizu, Gaku Oguri, Toshiya Kojima, Eriko Hasumi, Katsuhito Fujiu, Hiroyuki Morita, and Issei Komuro. Prevalence, associated factors, and echocardiographic estimation of left atrial hypertension in patients with atrial fibrillation. Journal of the American Heart Association, 12(18), 2023.

VQ Méndez, PS Mendoza, and DH Vizcaino. Interoperabilidad: un reto que trasciende el intercambio de información y datos. Datos, información, tendencias, 11, 2018.

Karen Avila, Paul Sanmartin, Daladier Jabba, and Miguel Jimeno. Applications based on service-oriented architecture (soa) in the field of home healthcare. Sensors, 17(8):1703, 2017.

J. Orozco, L. Rodríguez, and C. Viloria-Nuñez. Medibot: Intelligent virtual assistant for personalized medical care and guidance. In 2024 IEEE Technology and Engineering Management Society (TEMSCON LATAM), pages 1–6. IEEE, 2024.

Vladimir Quintero, Cesar Chevel, and Paul Sanmartin-Mendoza. Analysis on the interoperability of health information systems. 2024 IEEE Technology and Engineering Management Society (TEMSCON LATAM), pages 1–6, 2024.

D. E. C. L. A. R. A. C. I. Ó. N. DE PRINCIPIOS. Ley 23 de 1981, 1981.

J. E. Díaz-Realpe, J. Muñoz-Martínez, and C. H. Sierra-Torres. Factores de riesgo para enfermedad cardiovascular en trabajadores de una institución prestadora de servicios de salud, colombia. Revista de Salud pública, 9:64–75, 2007.

P. Cullen, H. Schulte, and G. Assmann. The münster heart study (procam), 1997.

R. Kimball and J. Caserta. The data warehouse ETL toolkit. John Wiley & Sons, 2004.

P. Salazar, C. Manterola, G. Quiroz, N. García, T. Otzen, M. Mora, and G. Duque. Estudios de cohortes. 1 parte. descripción, metodología y aplicaciones. Revista de cirugía, 71(5):482–493, 2019.

Gabriela González. Estudio prospectivo: características, ejemplos y limitaciones, 2021. Lifeder. Recuperado de https://www.lifeder.com/estudio-prospectivo/.

B. Analysts, C. Howson, J. Richardson, R. Sallam, and A. Kronz. Magic quadrant for analytics and business intelligence platforms, 2020.

República de Colombia. Ley 2015, historia clínica electrónica interoperable, 2020. Congreso de la República.