Determination of Significant Features for Building an Efficient Heart Disease Prediction System
Ekta Maini1, Bondu Venkateswarlu2, Arbind Gupta3
1Ekta Maini, Department of Computer Science &Engineering Dayananda Sagar University, Bengaluru, India
2Bondu Venkateswarlu, Department of Computer Science & Engineering Dayananda Sagar University, Bengaluru, India.
3Arbind Gupta, Department of Computer Science &Engineering, Dayananda Sagar College of Engineering, Bengaluru, India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 4499-4504 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3393078219/19©BEIESP | DOI: 10.35940/ijrte.B3393.078219
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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 responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.
Index Terms: Feature Selection, Heart Disease Prediction Machine Learning.
Scope of the Article: Machine Learning