Heart Disease Prediction using Ensemble Learning Method
Ramatenki Sateesh Kumar1, S.Sameen Fatima2, Anna Thomas3
1R.Sateesh Kumar, M.Tech from JNTU, Hyderabad
2Dr.S.Sameen Fatima, Department of Computer Science and Engineering of Osmania
3Anna Thomas, M.Tech in Computer Science & Engineering, Department of Computer Science & Engineering, Vasavi College of Engineering, Hyderabad.
Manuscript received on May 18, 2020. | Revised Manuscript received on May 27, 2020. | Manuscript published on May 30, 2020. | PP: 2612-2616 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2997059120/2020©BEIESP | DOI: 10.35940/ijrte.A2997.059120
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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: The human heart is the very important organ in our body. The World Health Organization estimates 31% of deaths are due to heart disease taking an estimated 1.79 crore lives. Unhealthy lifestyle, family history of heart problems, stress, etc. are few risk factors for heart disease. In this paper we are proposing an ensembling classifier using K-NN , SVM, MK-NN and CART (Decision Tree algorithm) for the efficient prediction of heart disease. The performance and efficiency of the algorithms and ensembling classifier are evaluated. The results indicate that the proposed system was more accurate to determine the existence or non-existence of heart disease. Out of these algorithms, ensemble classifier predicts heart disease more accurately. The accuracy is above 93%.
Keywords: Heart disease, K Nearest Neighbor, Suport Vector Machine, Decision Tree, Classification
Scope of the Article: Classification