Heart Disease Prediction using Machine Learning Techniques

Authors

  • Ramesh Ponnala  Assistant Professor, Department of MCA, Chaitanya Bharathi Institute of Technology (A), Gandipet, Hyderabad, Telangana State, India
  • K. Sai Sowjanya  MCA Scholar, Chaitanya Bharathi Institute of Technology (A), Gandipet, Hyderabad, Telangana State, India

DOI:

https://doi.org/10.32628/IJSRST2183218

Keywords:

Cardiovascular Disease (CVD), Heart disease prediction, Machine learning, Hybrid ML Techniques, Classification, Prediction

Abstract

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

References

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International Journal of Scientific Research in Science and Technology

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Published

2021-06-30

Issue

Volume 8, Issue 4, July-August-2021

Section

Research Articles

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

How to Cite

[1]
Ramesh Ponnala, K. Sai Sowjanya "Heart Disease Prediction using Machine Learning Techniques" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 4, pp.42-47, July-August-2021. Available at doi : https://doi.org/10.32628/IJSRST2183218