Comparison of heart disease prediction between basics and a Hybrid machine learning (ML) technique

Heart disease is one of the main causes of death worldwide. Machine learning has been discovered tobe useful in creating predictions from massive amounts of data. We’ve also seen machine learning techniques used in recentadvances in a variety of fields such as medical, finance and even retail. In this research, we used a few traditional ML techniques which is K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and a Hybrid ML technique that combines Random Forest, SVM and K-NN. We achieved a great performance level with 63.33% accuracy rate of using the hybrid ML model in predicting heart disease. Before applying machine learning techniques, we used feature selection including BORUTA and RFE to identify the Top 10 variables from the dataset to compare with non-using feature selection to build an effective predictive model. Other thanthat, several performance metrics are used to evaluate the results.