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Coronary disease is predicted by classification technique. The data mining tool WEKA has been exploited for implementing Naïve Bayes classifier. Proposed work is trapped with a specific end goal to enhance the execution of models. For improving the classification accuracy Naïve Bayes is combined with Bagging and Attribute Selection. Trial results demonstrated a critical change over in the current Naïve Bayes classifier. This approach enhances the classification accuracy and reduces computational time. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Ascendable Clarification for Coronary Illness Prediction using Classification Mining and Feature Selection Performances" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26690.pdf |
