Prediction of Heart Stroke using A Novel Framework – PySpark
CH Sai Harish1, G Krishna Vamsi2, G J P Akhil3, J N V Hari Sravan4, V Mounika Chowdary
1Mr. Chitluri Sai Harish*, B. Tech, Research Associate, Department of Computer Science and Engineering from KL University.
2Mr. G gnana krishna vamsi, B. Tech, Research Associate, Department of Computer Science and Engineering from KL University.
3Mr. G jaya phani akhil, B. Tech, Research Associate, Department of Computer Science and Engineering from KL University.
4Mr. J n v hari sravan, B. Tech, Research Associate, Department of Computer Science and Engineering from KL University.
5Ms. V mounika chowdary, Assistant Faculty and Project guide for the undergraduate Students, Department of Computer Science and Engineering from KL University.
Manuscript received on March 31, 2021. | Revised Manuscript received on April 06, 2021. | Manuscript published on May 10, 2021. | Volume-1 Issue-2, May 2020. | PP: 1-4 | Retrieval Number: 100.1/ijpmh.B1002031221 | DOI: 10.35940/ijpmh.B1002.051221
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© The Authors. Published by Lattice Science Publication (LSP). 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 one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.
Keywords: Machine Learning, Decision Tree, Naive Bayes, Random Forest, Machine Learning, Heart Disease Prediction, Kaggle.