Resumen

IBARRA, Rodrigo; LEON, Jaime; AVILA, Iván  y  PONCE, Hiram. Cardiovascular Disease Detection Using Machine Learning. Comp. y Sist. [online]. 2022, vol.26, n.4, pp.1661-1668.  Epub 17-Mar-2023. ISSN 2007-9737.  https://doi.org/10.13053/cys-26-4-4422.

The detection of Cardiovascular Diseases (CVDs) prematurely is of great interest for the Healthcare Industry. According to the World Health Organization, heart diseases represent $32\%$ of global deaths by 2019. In this work, we propose building an interpretable machine learning model to detect CVDs. For this, we use a public dataset consisting of over 320 thousand records and 279 features. We explore the performance of three well-known classifiers and we build them using hyper-parameter techniques. For interpretability, feature relevance is tested. After the experimental results, we found Random Forest to performed the best with $94\%$ of accuracy and $81\%$ of area under the ROC curve. We also implement an easy web application as a tool for detecting CVDs using relevant features information.

Palabras llave : Machine learning; classification; heart disease.

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