Comparative analysis of supervised machine learning algorithms for heart disease detection

Resumen

This paper describes the most prominent algorithms of Supervised Machine Learning (SML), their characteristics, and comparatives in the way of treating data. The Heart Disease dataset obtained from Kaggle was used to determine and test its highest percentage of accuracy. To achieve the objective, Python sklearn libraries were used to implement the selected algorithms, evaluate and determine which algorithm is the one that obtains the best results, applying decision tree algorithms achieved the best prediction results.

Biografía del autor

Hector Daniel Huapaya

Member of the artificial intelligence research group of the faculty of systems engineering, department of software engineering at the National University Mayor de San Marcos, Lima, (Perú).

Ciro Rodriguez

Professor at the School of Software Engineering at the National University Mayor de San Marcos, Lima, (Perú).

Doris Esenarro

Professor at the Faculty of Environmental Engineering and Graduate School of the National University Federico Villarreal, Lima, (Perú).

Publicado
2020-04-30
Cómo citar
Huapaya, H. D., Rodriguez, C., & Esenarro, D. (2020). Comparative analysis of supervised machine learning algorithms for heart disease detection. 3C Tecnología. Glosas De Innovación Aplicadas a La Pyme, 233-247. Recuperado a partir de http://ojs.3ciencias.com/index.php/3c-tecnologia/article/view/1003