An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Abstract

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method. Results were recorded before and after the data preprocessing techniques were applied. Out of all the algorithms, gradient boosting achieved the highest accuracy rate of 92.20% for the proposed model. The proposed model yielded better results with gradient boosting in terms of precision, recall, and f1-score. It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.