Applying and Improving Accuracy of Heart Disease Prediction Model using Meta-classifiers and Ensemble Learning Methods with Feature Selection
Uma K1, M Hanumanthappa2
1Uma K, Research Scholar, Department of Computer Science and Applications, Bangalore University, Bangalore (Karnataka), India.
2Dr. M Hanumanthappa, Professor, Department of Computer Science and Applications, Bangalore University, Bangalore (Karnataka), India.
Manuscript received on 30 June 2022 | Revised Manuscript received on 17 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 172-176 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71890711222 | DOI: 10.35940/ijrte.B7189.0711222
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Abstract: 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.
Keywords: Machine Learning, Ensemble Method, Heart Disease, Meta-classifier, Feature Selection Methods.
Scope of the Article: Machine Learning