Ascendable Clarification for Coronary Illness Prediction using Classification Mining and Feature Selection Performances
Journal Title: International Journal of Trend in Scientific Research and Development - Year 2019, Vol 3, Issue 5
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 "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.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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- EP ID EP629213
- DOI 10.31142/ijtsrd26690
- Views 104
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