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Computación y Sistemas
On-line version ISSN 2007-9737Print version ISSN 1405-5546
Abstract
IBARRA, Rodrigo; LEON, Jaime; AVILA, Iván and PONCE, Hiram. Cardiovascular Disease Detection Using Machine Learning. Comp. y Sist. [online]. 2022, vol.26, n.4, pp.1661-1668. Epub Mar 17, 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 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 of accuracy and of area under the ROC curve. We also implement an easy web application as a tool for detecting CVDs using relevant features information.
Keywords : Machine learning; classification; heart disease.