Comparative analysis of supervised machine learning algorithms for heart disease detection

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Publicado en 3C Tecnología. Edición Especial/Special Issue – Abril/April 2020



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.


Palabras clave

Supervised machine learning, Heart disease, Decision tree algorithms, Prediction.

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