Ensemble Machine Learning-based Heart Disease Prediction with Hyper-tuning Parameters
Subhani Shaik, M. Niharika Reddy, Umera Tasneem, D. Sreeja
Asian Journal of Research in Computer Science · pp. 103–114 · Published 17 Apr 2025
10.9734/ajrcos/2025/v18i5642Abstract
Heart disease is the most dangerous issue in the world. The health sector also faces problems with this disease in the world. A serious investigation avoids the issue in a short time. Optimal prediction generates accurate results for this disease. This type of investigation helps doctors identify diseases and cures for health. Machine learning techniques play a crucial role in this scenario. In health industry utilizes such types of engineering techniques for speedy identification and recovery. Here we use ensemble learning with hyper-tuning parameters of the dataset. Our research observes that if I use the different machine learning models individually, then the accuracy for the decision tree is 70.37%, the Random Forest tree is 79.63%, the Support Vector Machine is 75.93%, and the Logistic Regression predicts 81.48%. But ensemble models of decision tree and Random Forest tree generate an accuracy rate is 71. 29%, and the SVM and LR accuracy rate is 76.85%.
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