An Adaptive Technique to Predict Heart Disease Using Hybrid Machine Learning Approach

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Published: Aug 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9s.7415
Keywords:
Modeling heart disease, classifying cardiovascular diseases, and using machine learning and feature selection (CVD)

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V. Chandra Shekhar Rao
Associate professor , Dept. of Computer Science and Engineering, Kakatiya Institute of Technology and Science Warangal, India.
Gurrapu Pavani
M.Tech Scholar, Dept. of Computer Science and Engineering, Kakatiya Institute of Technology and Science Warangal, India.
C. Srinivas
Associate professor , Dept. of Computer Science and Engineering, Kakatiya Institute of Technology and Science Warangal, India.

Abstract

cardiovascular disease is amongby far prevalent fatalities in today's society. Cardiovascular disease is extremely hard to predict using clinical data analysis. Machine learning (ML) hasproved to be useful for helping in judgement and predictions with the enormous amount data produced by the healthcare sectorbusiness. Furthermore, latest events in other IoT sectors have demonstrated that machine learning is used (IOT). Several studies have examined the use of MLa heart disease prediction. In this research, we describe a novel method that, by highlighting essential traits, can improvethe precision of heart disease prognosis. Numerous data combinations and well-known categorization algorithms are used to create the forecasting models. Using a decent accuracy of 88.7%, we raise the level of playusing a heart disease forecasting approach that incorporates a88.7% absolute certainty in a combination random forest and linear model. (HRFLM).

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How to Cite
Rao, V. C. S. ., Pavani, G. ., & Srinivas, C. . (2023). An Adaptive Technique to Predict Heart Disease Using Hybrid Machine Learning Approach. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 228–232. https://doi.org/10.17762/ijritcc.v11i9s.7415
Issue
Vol. 11 No. 9s (2023)
Section
Articles

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