Cardio Vascular Ailments Prediction and Analysis Based On Deep Learning Techniques

Authors

  • Riddhi Kasabe Dept. of Computer science Engineering, KJEI’s Trinity College of Engineering and Research, Pune, Maharashtra.
  • Geetika Narang Dept. of Computer science Engineering, KJEI’s Trinity College of Engineering and Research, Pune, Maharashtra.

Keywords:

Classification, Data Mining, Decision trees, Naive Bayes, Machine Learning, Heart diagnosis

Abstract

The process of data analyzing from various perspectives and combining it into useful information is called Data mining . It is  used  for effective prediction of heart  ailment.  It will be based  on  risk factor the heart ailments that can be defined very easily. The main objective of this project is to evaluate different classification techniques in heart diagnosis. Firstly, the heart numeric dataset is extracted and preprocessed. Then, using extraction the features that are conditioned, are found to be classified by machine learning. Compared to existing system; machine learning provides better results and efficiency. Post steps like data classification, data precision, performance criteria involving accuracy F-measure is to be calculated. Machine learning provides better results and performance of the system. The comparison measure signify that Random Forest is the best classifier that can be used for the diagnosis of heart ailment on the existing sample dataset.

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References

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Published

2021-05-25

How to Cite

[1]
R. Kasabe and G. . Narang, “Cardio Vascular Ailments Prediction and Analysis Based On Deep Learning Techniques”, Int J Eng and Appl Phys, vol. 1, no. 2, pp. 174–178, May 2021.

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