Key result
An RFE-GB machine learning model predicts cardiovascular disease with ~89% accuracy.
Why the study?
Early identification of cardiovascular disease risk factors can reduce mortality, and machine learning algorithms are a potential method for accurate cardiac disease prediction.
Does a recursive feature elimination-based gradient boosting (RFE-GB) machine learning model improve the accuracy of cardiovascular disease prediction compared to other machine learning approaches?
Does a recursive feature elimination-based gradient boosting (RFE-GB) machine learning model improve the accuracy of cardiovascular disease prediction compared to other machine learning approaches?
A novel machine learning approach using recursive feature elimination and gradient boosting achieved 88.8% accuracy in predicting cardiovascular disease.
Heart disease is one of the most frequent chronic ailments people suffer. Early identification can lower death rates by avoiding or lowering cardiovascular disease (CVD) severity. For detecting risk indicators, machine learning algorithms are a potential way. To acquire accurate cardiac disease prediction, this work introduces a recursive feature elimination-based gradient boosting (RFE-GB) approach. The outcomes were evaluated using the patients' health records, including crucial CVD characteristics. The prediction model was built using many additional machine learning approaches, and the results were compared to the suggested model. The combined recursive feature removal and gradient boosting approach deliver the maximum accuracy, according to the findings of this suggested model (88.8 %). Furthermore, the presented RFE-GB method was determined to be superior and had acquired a significant gain over previous strategies, with an area under the curve of 0.84. The developed RFE-GB method will thus be a useful model for predicting and treating CVD.
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Prasannavenkatesan Theerthagiri (2022) studied Cardiovascular disease. Recursive feature elimination-based gradient boosting (RFE-GB) vs. Other machine learning approaches was evaluated on Cardiac disease prediction accuracy (AUC 0.84). A recursive feature elimination-based gradient boosting (RFE-GB) model predicted cardiovascular disease with 88.8% accuracy and an area under the curve of 0.84.
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