Morality Prediction Model in Cardiovascular Disease with Significant Feature Selection and Hybrid KNN Classification Technique
C.Sowmiya1, P. Sumitra2
1C.Sowmiya *, Ph.D Research Scholar, PG and Research Department of Computer Science and Applications Vivekananda College of Arts and Sciences for Women (Autonomous), Elayampalayam, Tiruchengode-637205, Tamil Nadu, India.
2P. Sumitra, Assistant Professor in PG and Research Department of Computer Science and Applications, Vivekanandha College of Arts and Sciences for Women, Elayampalayam, Tiruchengode(TK), Namakkal (DT), Tamil Nadu, India.
Manuscript received on September 18, 2019. | Revised Manuscript received on 23 September, 2019. | Manuscript published on October 10, 2019. | PP: 5497-5502 | Volume-8 Issue-12, October 2019. | Retrieval Number: K23440981119/2019©BEIESP | DOI: 10.35940/ijitee.K2344.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays morality rate is increasing globally due to heart disease. It is one of the leading health risk facing men today. So early detection of heart disease assist the patients to maintain a healthy life style. Several techniques are used in the medical field to detect or diagnose disease in view of patient family health history and some other aspects. However, developing a system to predict the heart diseases without any medical tests is still challenging. Machine learning (ML) approaches is suitable and effective in providing decision and prediction from enormous health care data. Several previous researches provide an overall view in ML methods for disease prediction but the accuracy of prediction is still needed to be improved. In this study, a novel framework is presented that intent at removing the unwanted features with Bacterial Colony Optimization algorithm and applies the Hybrid KNN algorithm with great accuracy in identifying the heart disease. This prediction model is developed with UCI Cleveland dataset with several known classification approaches. An enhanced model is presented with 99.83% accuracy in heart disease prediction. The presented study is compared with other classification approaches.
Keywords: Data Mining, Machine Learning, Feature Selection, Heart Disease, Hybrid KNN.
Scope of the Article: Machine Learning