Heart Disease Prediction

Authors

  • S Vinothini

  • Ishaan Singh

  • Sujaya Pradhan

  • Vipul Sharma

Received date: July 29, 2018

Accepted date: July 29, 2018

Published date: July 20, 2018

DOI:

https://doi.org/10.14419/ijet.v7i3.12.16494

Keywords:

.

Abstract

Machine learning algorithm are used to produce new pattern from compound data set. To cluster the patient heart condition to check whether his /her heart normal or stressed or highly stressed k-means clustering algorithm is applied on the patient dataset. From  the results of clustering ,it is hard to elucidate and to obtain the required conclusion from these clusters. Hence another algorithm, the decision tree, is used for the exposition of the clusters of . In this work, integration of decision tree with the help of k-means algorithm is aimed. Another learning technique such as SVM and Logistics regression is used. Heart disease prediction results from SVM and Logistics regression were compared.

 

References

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How to Cite

Vinothini, S., Singh, I., Pradhan, S., & Sharma, V. (2018). Heart Disease Prediction. International Journal of Engineering and Technology, 7(3.12), 750-753. https://doi.org/10.14419/ijet.v7i3.12.16494

Received date: July 29, 2018

Accepted date: July 29, 2018

Published date: July 20, 2018