The Random Forest algorithm achieved the highest classification accuracy of 94.96% for heart disease prediction when using 8 selected features and an 80-20 data split.
Machine learning, specifically the random forest algorithm combined with feature selection, can accurately predict heart disease risk using a reduced set of 6 to 8 key clinical attributes.
The heart disease has been one of the major causes of death worldwide. The heart disease diagnosis has been expensive nowadays, thus it is necessary to predict the risk of getting heart disease with selected features. The feature selection methods could be used as valuable techniques to reduce the cost of diagnosis by selecting the important attributes. The objectives of this study are to predict the classification model, and to know which selected features play a key role in the prediction of heart disease by using Cleveland and statlog project heart datasets. The accuracy of random forest algorithm both in classification and feature selection model has been observed to be 90–95% based on three different percentage splits. The 8 and 6 selected features seem to be the minimum feature requirements to build a better performance model. Whereby, further dropping of the 8 or 6 selected features may not lead to better performance for the prediction model.
Reddy et al. (Fri,) conducted a other in Heart Disease (n=1,190). Random Forest algorithm with feature selection vs. Other machine learning algorithms (KNN, SVM, Naive Bayes, Neural Network) was evaluated on Classification accuracy for heart disease prediction. The Random Forest algorithm achieved the highest classification accuracy of 94.96% for heart disease prediction when using 8 selected features and an 80-20 data split.
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