Ensemble Model for Heart Disease Prediction

Abstract

For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. The heart is one of the essential parts of the human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical decision support systems to enhance the ability to diagnose and predict heart disease in humans. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researcher looks at how to use the ensemble model, which proposes a more stable performance than the use of a base learning algorithm and these lead to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher Bagging meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, according to the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has a high prediction probability score in the implementation of heart disease prediction.