Open AccessEFFECTIVE HEART DISEASE PREDICTION USING HYBRID MACHINE LEARNING TECHNIQUES
Author(s) -
M Megha Ganesh Chandra
Publication year - 2022
Publication title -
indian scientific journal of research in engineering and management
Language(s) - English
Resource type - Journals
ISSN - 2582-3930
DOI - 10.55041/ijsrem12462
Subject(s) - machine learning , computer science , random forest , heart disease , artificial intelligence , disease , predictive modelling , internet of things , support vector machine , data mining , medicine , embedded system
Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM)