A Comparative study of Data Classification Techniques for Coronary Artery Disease

Authors

  • Vinod Babu P  Department of CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India
  • B S V Prasad  Department of CSE, Usha Rama College of Engineering and Technology, Telaprolu, Andhra Pradesh, India
  • Ch Seshadri Rao  Department of CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India

Keywords:

Data Mining, Heart Attack, Classification, Support Vector Machine (SVM)

Abstract

Now a day, the heart attack is one of the deadliest diseases patients face. This disease attacks a person so instantly that it hardly gets any time to be treated with. If you're 35-70 you should go for a heart health check-up to assess your risk of having a heart attack in the next 10 years. Identifying and managing a condition such as high blood pressure or high cholesterol or hypertension could help lower your chances of having a risk in the heart attack in the future. Data mining techniques can help in predicting the risk of heart attack of the person for the next ten years. Data classification algorithms such as Support Vector Machine (SVM), Logistic Regression, and Decision tree can help in classifying the given patients data can help in the prediction of the heart attack in next ten years. Results show the accuracy percentage of the prediction whether a person can have the heart attack in next ten year or not based on their medical data.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-06-30

Issue

Volume 4, Issue 9, July-August-2018

Section

Research Articles

License

Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vinod Babu P, B S V Prasad, Ch Seshadri Rao, " A Comparative study of Data Classification Techniques for Coronary Artery Disease , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.28-34, July-August-2018.