Prediction of Coronary Artery Disease Using Machine Learning

Authors

  • Ajay Gouda Yallappagoudar Department of Computer Science Engg., Dayananda Sagar College of Engineering, Bangalore, India
  • C. M. Arun Department of Computer Science Engg., Dayananda Sagar College of Engineering, Bangalore, India
  • Shashank Gangadhar Department of Computer Science Engg., Dayananda Sagar College of Engineering, Bangalore, India
  • Vishal Agarwal Department of Computer Science Engg., Dayananda Sagar College of Engineering, Bangalore, India
  • M. Anitha Department of Computer Science Engg., Dayananda Sagar College of Engineering, Bangalore, India

Keywords:

Coronary Artery Disease, Cleveland dataset, Machine Learning

Abstract

For years, there has been a lot of onus on implementation of machine learning and its application techniques in the Medical field and is often referred to be a valuable rich information. Coronary Artery Disease (CAD) is one of the major causes of death all around the world and early detection can prevent it. The aim of this study is to predict CAD using historical medical data. The dataset is retrieved from the “Cleveland Dataset” which is openly available and provided by the University of California Irvine (UCI) Machine Learning Repository and it contains 303 instances with a total of 14 attributes. The algorithm is trained with the dataset and using many machine learning methods maximum accuracy will be found out. According to a survey by WHO, doctors can predict the disease with approximate of 67% and we here try to achieve accuracy greater than 90% of accuracy thus saving many lives. Using cutting edge ML technology of text to speech conversion, we improvise the quality of assistance. We also focus on a designing a webpage which receives attributes as input and give them an accurate result.

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Published

17-01-2021

How to Cite

[1]
A. G. Yallappagoudar, C. M. Arun, S. Gangadhar, V. Agarwal, and M. Anitha, “Prediction of Coronary Artery Disease Using Machine Learning”, IJRAMT, vol. 1, no. 3, pp. 4–7, Jan. 2021.

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Articles