Heart Disease Risk Predictor using Support Vector Machine
Pooja Saharan1, Rashmi Mishra2, Charvee Garg3, Aman Payal4 

1Pooja Saharan, Department of Computer Science & Engineering, ABESEC, Ghaziabad, India.
2Rashmi Mishra, Department of Computer Science & Engineering, ABESEC, Ghaziabad, India, Rashmi.
3Charvee Garg, Department of Computer Science & Engineering, ABESEC, Ghaziabad, India, Charvee.
4Aman Payal, Department of Computer & Engineering, ABESEC, Ghaziabad, India, Aman.

Manuscript received on 15 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 July 2019 | PP: 4626-4636 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3423078219/19©BEIESP | DOI: 10.35940/ijrte.B3423.078219
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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: Nearly 17.5 million deaths occur due to cardiovascular diseases throughout the world. If we could create such a mechanism or system that could tell people about their heart condition based on their medical history and warn them of any risk than it could be of huge help. In our work, we will use machine learning algorithms to forecast the heart disease risk factor for a person depending upon some attributes in their medical history. The data mining technique Naive Bayes, Decision tree, Support Vector Machine, and Logistic Regression is analyzed on the Heart disease database. The accuracy of different algorithms is measured and then the algorithms are compared.
Index Term: Data Mining, Heart Disease, Jupiter Notebook, SVM (Support Vector Machine).

Scope of the Article: Data Mining