| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560754 |
| ISSN: | 1405-5546 |
| Autores: | Ibarra, Rodrigo1 León, Jaime1 Ávila, Iván1 Ponce, Hiram1 |
| Instituciones: | 1Universidad Panamericana, Facultad de Ingeniería, México |
| Año: | 2022 |
| Periodo: | Oct-Dic |
| Volumen: | 26 |
| Número: | 4 |
| Paginación: | 1661-1668 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | 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. |
| Keyword: | Machine learning, Classification, Heart disease |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
