Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique

Abstract

Background: 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. Methods: 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. Results: 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. Conclusion: The developed RFE-GB method will thus be a useful model for predicting and treating CVD.

Keywords

• Heart diseases
• Feature ranking
• Machine learning
• Recursive feature elimination
• Gradient boosting