Enhancing Heart Disease Prediction Accuracy Through Hybrid Machine Learning Methods





Cardiovascular disease, Hybrid Random Forest, Hybrid Random Forest with Linear Model, Support Vector Machines, Artificial Neural Network, Naive Bayes


INTRODUCTION: Over the past few decades, heart disorders have been the leading cause of mortality worldwide. People over 55 must get a thorough cardiovascular examination to prevent heart disease or coronary sickness and identify early warning signs. To increase the ability of healthcare providers to recognize cardiovascular illness, researchers and experts have devised a variety of clever ways.

OBJECTIVES: The goal of this research was to propose a robust strategy for cardiac issue prediction utilizing machine learning methods. The healthcare industry generates a massive quantity of data and machine learning has proved effective in making decisions and generating predictions with this data. 

METHODS: Al has been exhibited to be useful in helping with forecast and decision-production because of the tremendous measure of information made by the medical services a 20 Few explorers have inspected the capability of Al to figure out heart disease. In this article, we suggest a creative strategy.  to improve the exactness of cardiovascular sickness forecasts by finding basic highlights utilizing Al systems.

CONCLUSION: There is a lot of promise and possibility in using machine learning techniques to forecast cardiac disease. By means of examining a range of datasets and applying multiple machine-learning methods. Alongside various element blends and not able arrangement procedures, the expectation model is presented. We accomplish a better exhibition level with a Crossbreed Irregular Woods, with a Direct Model as our coronary illness forecast model.


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How to Cite

N. S. Gupta, S. K. Rout, S. Barik, R. R. Kalangi, and B. Swampa, “Enhancing Heart Disease Prediction Accuracy Through Hybrid Machine Learning Methods ”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.