A Hybrid Approach for Heart Disease Prediction

Authors

  • Aamir Khan  CSA Department, ITM University, Gwalior, Madhya Pradesh, India
  • Dr. Sanjay Jain  CSA Department, ITM University, Gwalior, Madhya Pradesh, India

DOI:

https://doi.org/10.32628/CSEIT217531

Keywords:

Heart Disease prediction, MLP, Decision Tree, Naïve Bayes, Random Forest, Logistic Regression

Abstract

The data mining (DM) is a process that deals with mining of valuable information from the rough data. The method of prediction analysis (PA) is implemented for predicting the future possibilities on the basis of current information. This research work is planned on the basis of predicting the heart disease. The coronary disorder can be forecasted in different phases in which pre-processing is done, attributes are extracted and classification is performed. The hybrid method is introduced on the basis of RF and LR.The Random Forest classification is adopted to extract the attributes and the classification process is carried out using logistic regression. The analysis of performance of introduced system is done with regard to accuracy, precision and recall. It is indicated that the introduced system will be provided accuracy approximately above 90% while predicting the heart disease.

References

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

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Published

2021-10-30

Issue

Volume 7, Issue 5, September-October-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Aamir Khan, Dr. Sanjay Jain, " A Hybrid Approach for Heart Disease Prediction" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 5, pp.95-101, September-October-2021. Available at doi : https://doi.org/10.32628/CSEIT217531