Heart Disease Prediction using Hybrid Machine Learning Techniques

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Goli Manisha, Gonamanda Anupama, Gudimetla Rajendra Sai Nithin, Manepalli Mohan Durga Prasad

Abstract

One of the leading causes of death in the modern world is coronary artery disease. Clinical data analysis faces a major challenge in predicting cardiovascular disease. It has been proven that machine learning (ML) can be used to make predictions and decisions based on the vast amounts of data generated by the healthcare industry. ML techniques have also been used in recent developments in various IoT areas (IoT). Only a sliver of the potential of ML to predict heart disease has been explored so far in various studies. Machine learning techniques are used to find significant features in this paper, which improves the accuracy of cardiovascular disease prediction. A variety of feature combinations and well-known classification techniques are used to build the prediction model. The prediction model for heart disease using the hybrid random forest with a linear model produces an improved performance level with an accuracy level of 88.7 percent (HRFLM).

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