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Publication Type : Conference Paper
Publisher : Proceedings of the International Conference on Soft Computing Systems
Source : Proceedings of the International Conference on Soft Computing Systems, Springer (2016)
Keywords : Coronary heart disease Prediction Classification Random forest Cleveland heart data Risk factors
Campus : Amritapuri, Coimbatore
School : Department of Computer Science and Engineering, School of Engineering
Center : Computational Engineering and Networking
Department : Computer Science, Mathematics
Verified : Yes
Year : 2016
Abstract : Heart diseases are the major cause of death in today’s modern age. Coronary heart disease is one among them. This disease attacks the normal person instantly. Proper diagnosis and timely attention to the patients reduce mortality rate. Proper diagnosis has become a challenging task for the medical practitioners. The cost involved in the immediate treatment or intervention methods are also very expensive. Early diagnosis of the disease using mining of medical data prevents the inattention of occurrence of sudden CHD events. Today, almost all hospitals are using hospital information system and it has huge volume of patient records. This study results in the development of a decision support system using machine intelligence techniques applied on the medical records stored in hospital databases. Classification algorithms are used to evaluate the accuracy of the early prediction of coronary heart events. The classification techniques analyzed are K-nearest neighbor, decision tree-C4.5, Naive Bayes, and the random forest. The accuracy of each technique is found to be 77, 81, 84, and 89nbsp;%, respectively. In this study 10-fold cross-validation method is used to measure the unbiased estimate of these prediction models.
Cite this Research Publication : Ani R., Augustine, A., Akhil, N. C., and Dr. Deepa Gopakumar O. S., “Random Forest Ensemble Classifier to Predict the Coronary Heart Disease Using Risk Factors”, in Proceedings of the International Conference on Soft Computing Systems, 2016.