Comparative of classification algorithm: Decision tree, SVM, and KNN for heart diseases prediction

Heart disease is one of the diseases that every year the death rate is always increasing. Early diagnosis of heart disease is very important because the patient is treated more quickly. In this study, machine learning classification algorithms Decision Tree, SVM, and KNN were used for the early diagnosis of heart disease. The manufacturing process model is done with the following steps: data collecting, pre-processing, model building, comparison of models, and evaluation. The results showed that the KNN algorithm has the best accuracy in predicting with a precision value of 90.16%. The Decision Tree algorithm has a prediction accuracy of 75.41%, and the SVM algorithm has a prediction accuracy value of 83.61%. This shows that the KNN method is the most effective method among the other two methods in diagnosing a person whether they have heart disease or not.