Enhanced Heart Disease Prediction Based on Machine Learning and ?2 Statistical Optimal Feature Selection Model

Automatic heart disease prediction is a major global health concern. Effective cardiac treatment requires an accurate prognosis. Therefore, this paper proposes new classification model based on the support vector machine (SVM) algorithm for improved detection. To increase accuracy, ?2 statistical optimum feature selection technique was used. The suggested model’s performance then validated by comparing it to traditional models using several measures. proposed increased accuracy from 85.29% 89.7%. Additionally, componential load reduced half. This result indicates that our system outperformed other state-of-the-art methods in predicting disease.