| Autor: |
Barry KA; LPAIS Laboratory, Faculty of Sciences, USMBA, Fez, Morocco., Manzali Y; LPAIS Laboratory, Faculty of Sciences, USMBA, Fez, Morocco., Flouchi R; Laboratory of Microbial Biotechnology and Bioactive Molecules, Science and Technologies Faculty, Sidi Mohamed Ben Abdellah University, Fez, Morocco., Balouki Y; Labo: Mathematics, Computer Science and Engineering Sciences(MISI), Settat, Morocco., Chelhi K; The logistics center of excellence, Higher School of Textile and Clothing Industries(ESITH Casablanca), Casablanca, Morocco., Elfar M; LPAIS Laboratory, Faculty of Sciences, USMBA, Fez, Morocco. |
| Abstrakt: |
Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique. |
