Machine Learning-Based Comparative Study For Heart Disease Prediction

Heart disease is one of the most common causes death globally. In this study, machine learning algorithms and models widely used in literature to predict heart have been extensively compared, a hybrid feature selection based on genetic algorithm tabu search methods developed. The proposed system consists three components: (1) preprocess datasets, (2) with algorithm, (3) classification module. tested using different detailed comparisons analysis were presented. experimental results show that Random Forest more successful than Adaboost, Bagging, Logitboost, Support Vector Cleveland Statlog datasets.