Ensemble Machine Learning-based Heart Disease Prediction with Hyper-tuning Parameters
Subhani Shaik
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
M. Niharika Reddy *
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
Umera Tasneem
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
D. Sreeja
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
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%.
Keywords: Machine learning, heart disease prediction, technology, model