Analysis of Prediction Accuracy of Heart Diseases using Supervised Machine Learning Techniques for Developing Clinical Decision Support Systems
Kiruthikaa K V1, Vijay Franklin J2, Yuvaraj S3
1Kiruthikaa K V, Department of Computer Science Engineering, Bannari Amman Institute of Technology, Sathyamangalam (Tamil Nadu), India.
2Dr. Vijay Franklin J, Department of Computer Science Engineering, Bannari Amman Institute of Technology, Sathyamangalam (Tamil Nadu), India.
3Yuvaraj S, Department of Computer Science Engineering, Bannari Amman Institute of Technology, Sathyamangalam (Tamil Nadu), India.
Manuscript received on 16 December 2018 | Revised Manuscript received on 27 December 2018 | Manuscript Published on 09 January 2019 | PP: 433-437 | Volume-7 Issue-4S November 2018 | Retrieval Number: E2046017519/19©BEIESP
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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: Heart diseases are taking on hands as the vital mortality deciding factor in the current era. Most of the people around the world are experiencing a time-scheduled and stressful work life, which often leads to increase in the percentage of healthy people affected by heart diseases. It is mandatory to solve this raising issue by predicting the occurrence of the disease as earlier as possible with the help of variety of available solutions. Machine learning techniques can be applied to analyze and predict whether a person is likely to have heart disease or not. In this paper, we made a detailed investigation on prediction accuracy rate of heart diseases using different supervised machine learning techniques, which will pave the way for researchers to choose the efficient technique(s) in order to design and develop clinical decision support systems that predicts the occurrence of heart diseases in people efficiently.
Keywords: Heart Disease, Machine Learning, Prediction Accuracy, Clinical Decision Support Systems.
Scope of the Article: Machine Learning