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| Titre: | Using Machine Learning for Heart Disease Prediction |
| Auteur(s): | Salhi, Dhai Eddine
Tari, Abdelkamel
Tahar Kechadi, M. |
| Mots-clés: | Machine Learning
Heart disease
Prediction
Detection |
| Date de publication: | 2021 |
| Editeur: | Springer |
| Collection/Numéro: | International Conference on Computing Systems and Applications CSA 2020: Advances in Computing Systems and Applications;pp. 70-81 |
| Résumé: | In this paper we carried out research on heart disease from data analytics point of view. Prediction of heart disease is a very recent field as the data is becoming available. Other researchers have approached it with different techniques and methods. We used data analytics to detect and predict disease’s patients. Starting with a pre-processing phase, where we selected the most relevant features by the correlation matrix, then we applied three data analytics techniques (neural networks, SVM and KNN) on data sets of different sizes, in order to study the accuracy and stability of each of them. Found neural networks are easier to configure and obtain much good results (accuracy of 93%). |
| URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7149 |
| Collection(s) : | Communications Internationales
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