An empirical study on machine learning algorithms for heart disease prediction

In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of feature used for training model has a significant impact on outcome predictive model. Thus, this study focuses exploring features performance prediction by employing recursive elimination with cross-validation (RFECV). Furthermore, explores effect output. The experimentation obtained from University California Irvine (UCI) dataset. experiment implemented using support vector (SVM), logistic regression (LR), decision tree (DT), random forest (RF) are employed. SVM, LR, DT, RF models. result appears to prove significantly affects Overall, proves outperforms as compared other algorithms. conclusion, 99.7% achieved RF.