1. BAMANGA, MAHMUD AHMAD - Computer Science Department, Federal University of Lafia, Nigeria.
2. AHMADU, ASABE SANDRA - Computer Science Department, Modibbo Adama University, Yola, Nigeria.
3. MUSA, YUSUF MALGWI - Computer Science Department, Modibbo Adama University, Yola, Nigeria.
4. KAMALU ALIYU BABANDOâ€‹ - Computer Science Department, Taraba State Polytechnic, Jaling, Nigeria.
Machine learning techniques are commonly used in clinical decision support systems for the identification and prediction of various diseases. Since heart disease is the leading cause of death for both men and women around the world. Because the heart is such an important part of the human body, it is one of the most pressing medical concerns. To improve the ability to diagnose and predict heart disease in humans, several researchers have developed intelligent medical decision support systems. 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 look at how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads 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 aggregate Naïve Bayes, Support Vector Machine and Decision Tree with Adaboost and Bagging. 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 the highest prediction accuracy of 91% compared to adaboost with a prediction accuracy of 88% in the implementation of heart disease prediction.
PREDICTIVE ANALYSIS OF HEART DISEASE USING SELECTED MACHINE LEARNING META - ALGORITHMS