Early Detection of Cardiovascular Disease using Machine learning Techniques an Experimental Study
Najmu Nissa1, Sanjay Jamwal2, Shahid Mohammad3
1Najmu Nissa*, Department of Computer Sciences, BGSBU University Rajouri, J&K, India.
2Sanjay Jamwal, Sr. Assistant Professor, Department of Computer Sciences BGSBU Rajouri, J&K, India.
3Shahid Mohammad, Department of Computer Sciences, BGSBU University Rajouri, J&K, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 635-641 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4657099320 | DOI: 10.35940/ijrte.C46570.99320
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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: Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact our lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Therefore, the prediction of this disease is of prime importance as it will enable one to take a proper and needful approach at a proper time. Data mining and machine learning are taking out and refining of useful information from a massive amount of data. It is a basic and primary process in defining and discovering useful information and hidden patterns from databases. The flexibility and adaptability of optimization algorithms find its use in dealing with complex non -linear problems. Machine Learning techniques find its use in medical sciences in solving real health-related issues by early prediction and treatment of various diseases. In this paper, six machine learning algorithms are used and then compared accordingly based on the evaluation of performance. Among all classifiers, decision tree outperforms over the other algorithms with a testing accuracy of 97.29%.
Keywords: Heart Disease, Machine Learning Models, Python, Spyder.