Heart Disease Prediction Using Hybrid Classifier

Mudassir Ahmad, Dr. Sonia Vatta

Abstract The data mining is the approach which can extract useful information from the data. This research work is related to heart disease prediction. The prediction analysis is the approach which can predict future possibilities based on the current information. The hybrid classifier is designed in this research work, for the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. The random forest classifier extract the information and decision tree generate final classifier result. The proposed hybrid model is implemented in python and results are compared with SVM classifier. It is analyzed that hybrid classifier has maximum performance as compared to SVM classifier Keywords: Hybrid classifier, SVM, Heart disease prediction, Data Mining

Title:	Heart Disease Prediction Using Hybrid Classifier
Author Name:	Mudassir Ahmad, Dr. Sonia Vatta
Abstract:	Abstract The data mining is the approach which can extract useful information from the data. This research work is related to heart disease prediction. The prediction analysis is the approach which can predict future possibilities based on the current information. The hybrid classifier is designed in this research work, for the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. The random forest classifier extract the information and decision tree generate final classifier result. The proposed hybrid model is implemented in python and results are compared with SVM classifier. It is analyzed that hybrid classifier has maximum performance as compared to SVM classifier Keywords: Hybrid classifier, SVM, Heart disease prediction, Data Mining
Cite this article:	Mudassir Ahmad, Dr. Sonia Vatta , " Heart Disease Prediction Using Hybrid Classifier " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 8, Issue 8, August 2019 , pp. 039-046 , ISSN 2319 - 4847.
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