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Ascendable Clarification for Coronary Illness Prediction using Classification Mining and Feature Selection Performances

. International Journal of Trend in Scientific Research and Development, 3 (5): 1588-1592 (August 2019)
DOI: https://doi.org/10.31142/ijtsrd26690

Abstract

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 Äscendable 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.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26690/ascendable-clarification-for-coronary-illness-prediction-using-classification-mining-and-feature-selection-performances/d-haripriya

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