Open AccessHeart Disease Prediction using Ensemble Learning Method
Author(s) -
Ritesh Kumar,
Dr.S.Sameen Fatima,
Anna Thomas
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a2997.059120
Subject(s) - heart disease , classifier (uml) , support vector machine , decision tree , artificial intelligence , computer science , machine learning , ensemble learning , crore , disease , pattern recognition (psychology) , medicine , poverty , economics , economic growth
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[17] , SVM[18], MK-NN and CART[19] (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%.