Prediction of Heart Diseases Using Data Mining and Machine Learning Algorithms and Tools

Authors

  • M. Nikhil Kumar  Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
  • K. V. S. Koushik  Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
  • K. Deepak  Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

Keywords:

Data Mining, Machine Learning, Decision Tree, Heart Disease

Abstract

Data mining is the most popular knowledge extraction method for knowledge discovery (KDD). Machine learning is used to enable a program to analyze data, understand correlations and make use of insights to solve problems and/or enrich data and for prediction. Data mining techniques and machine learning algorithms play a very important role in medical area. The health care industry contains a huge amount of data. But most of it is not effectively used. Heart disease is one of the main reason for death of people in the world. Nearly 47% of all deaths are caused by heart diseases. We use 8 algorithms including Decision Tree, J48 algorithm, Logistic model tree algorithm, Random Forest algorithm, Naïve Bayes, KNN, Support Vector Machine, Nearest Neighbour to predict the heart diseases. Accuracy of the prediction level is high when using more number of attributes. Our aim is to perform predictive analysis using these data mining, machine learning algorithms on heart diseases and analyze the various mining, Machine Learning algorithms used and conclude which techniques are effective and efficient.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

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Copyright (c) IJSRCSEIT

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

How to Cite

[1]
M. Nikhil Kumar, K. V. S. Koushik, K. Deepak, " Prediction of Heart Diseases Using Data Mining and Machine Learning Algorithms and Tools" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.887-898, March-April-2018.