Diagnosis of heart disease using oversampling methods and decision tree classifier in cardiology

PURPOSE: Heart disease is one of the most prevalent and critical diseases that endangers the lives of human beings. In addition to clinical diagnosis, machine learning and deep learning-based approaches are vital in the diagnosis of heart disease. METHOD: This paper proposes a balanced and optimized machine-learning algorithm for heart disease detection. This technique combines oversampling techniques, attribute pruning, CART decision tree classifier, and rule pruning through hyper-parameter tuning to identify the presence of heart disease. It further identifies the key attributes that contribute to the occurrence of heart malfunctioning. RESULTS: Experimental results show that SMOTE sampled dataset exhibits effective performance when implemented using a balanced and optimized machine learning algorithm, with an improvement of 11%, 75%, 62%, and 71% in accuracy, precision, recall, and f1 scores when compared with the dataset that was not subjected to sampling. The algorithm works effectively when the imbalance ratio is high for a dataset. CONCLUSION: The algorithm can be used to predict the presence of heart disease even in highly imbalanced datasets and identify critical features in the malfunctioning of the heart.