Applying and Improving Accuracy of Heart Disease Prediction Model using Meta-classifiers and Ensemble Learning Methods with Feature Selection

-449210151823c775641-7b20 20220730044337838 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 07 30 2022 11 2 Applying and Improving Accuracy of Heart Disease Prediction Model using Meta-classifiers and Ensemble Learning Methods with Feature Selection Research Scholar, Department of Computer Science and Applications, Bangalore University, Bangalore (Karnataka), India. Uma K Dr. M Hanumanthappa Professor, Department of Computer Science and Applications, Bangalore University, Bangalore (Karnataka), India. Healthcare industry is a significant sector for producing an enormous amount of data daily. The lack of helpful information is the primary motive for introducing machine learning or data mining techniques for extracting the required pattern needed to make a decision. Globally, heart disease is the leading cause of death. Prediction of heart disease early may help the survival of the patient life. This paper explores the machine learning technologies, ensemble learning, and meta-classifier to predict heart disease with feature selection methods to improve the accuracy. It presents a performance comparison between classifiers, ensemble learning methods, and meta-classifier 07 30 2022 172 176 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7189.0711222 https://www.ijrte.org/portfolio-item/b71890711222/