Key result
A prediction model for heart disease using a hybrid random forest with a linear model (HRFLM) achieved an enhanced accuracy level of 88.7%.
Why the study?
Predicting cardiovascular disease from healthcare data is a critical challenge, and existing studies offer only limited insight into using machine learning techniques for this prediction.
Does a hybrid random forest with a linear model (HRFLM) improve the accuracy of predicting cardiovascular disease?
Does a hybrid random forest with a linear model (HRFLM) improve the accuracy of predicting cardiovascular disease?
A novel hybrid machine learning model (HRFLM) demonstrates high accuracy (88.7%) in predicting heart disease, highlighting the potential of ML in clinical data analysis.
ML models for CVD prediction need prospective validation; leaves open clinical utility in IoT settings.
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)
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M Megha Ganesh Chandra (2022) studied Cardiovascular disease. Hybrid random forest with a linear model (HRFLM) vs. Several known classification techniques was evaluated on Prediction accuracy of cardiovascular disease. A prediction model for heart disease using a hybrid random forest with a linear model (HRFLM) achieved an enhanced accuracy level of 88.7%.
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