Best Feature Selection using Modified Whale Optimization Algorithm for Prediction of Heart Disease
M. Geethanjali1, P. Madhubala2

1M. Geethanjali, Assistant Professor, Department of Computer Science, St.Joseph’s College of Arts and Science for Women, Hosur (Tamil Nadu), India.

2Dr. P. Madhubala, HEAD & Assistant Professor, Department of Computer Science, Don Bosco College, Dharmapuri (Tamil Nadu), India.

Manuscript received on 12 January 2020 | Revised Manuscript received on 08 February 2020 | Manuscript Published on 20 February 2020 | PP: 410-414 | Volume-9 Issue-3S January 2020 | Retrieval Number: C10880193S20/2020©BEIESP | DOI: 10.35940/ijitee.C1088.0193S20

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Abstract: Coronary illness is the confusion of heart and blood veins. It is hard for restorative specialists and specialists to foresee precise about coronary illness determination. Information science is the majorobject in primary expectation and takes care of huge information issues nowadays. This examination paper portrays the expectation of coronary illness in restorative area by utilizing information science. The same number of inquiries about done research identified with that issue however the exactness of expectation is yet should have been improved. Thus, this exploration centers on highlight choice methods and calculations where numerous coronary illness datasets are utilized for experimentation investigation and to appearance the precision development. In this work Modified Whale Optimization (MWOA) is utilized for highlight determination reason. By utilizing the Rapid digger as apparatus; Random Forest, (ANN), Decision Tree(DT) and Naive Bayes(NB) calculations are utilized as highlight choice procedures and improvement is appeared in the outcomes by demonstrating the exactness. From the proposed investigation the Artificial Neural Network grouping system is document better outcomes regarding Accuracy, Recall, Precision and F-measure.

Keywords: Heart Disease Prediction, Feature Selection, Modified Whale Optimization (MWOA), Random Forest, Artificial Neural Network (ANN), Decision Tree (DT) and Naive Bayes (NB).
Scope of the Article: Regression and Prediction