Key result
An enhanced AdaBoost machine learning technique for predicting coronary artery disease achieved 88% accuracy and an AUC of 0.88, outperforming other classifiers.
Why the study?
Precise early prediction of cellular breakdown in coronary artery disease using machine learning techniques is needed to improve diagnosis and outcomes.
Does an enhanced AdaBoost machine learning technique accurately predict coronary artery disease using clinical, sensor, and laboratory data?
Does an enhanced AdaBoost machine learning technique accurately predict coronary artery disease using clinical, sensor, and laboratory data?
Effect estimate: AUC 0.88
An enhanced AdaBoost machine learning model demonstrated 88% accuracy in diagnosing coronary artery disease using non-invasive diagnostic and sensor data.
With the high speed in extensive grouping information medical services system, around 610,000 annual deaths, i.e., four in one death due to coronary artery disease (CAD), a total of 17.8 million deaths annually, and precise forecast in the discovery of cellular breakdown in the coronary artery disease at ahead of schedule symptoms, machine learning (ML) gives the most innovative possible solution. In this paper, we are aliening out of ML techniques to plan a design for effective classification of pictures and phases of cellular breakdown in coronary artery disease as far as possible. Here, we applied coronary artery disease diagnosed by exercise stress test and electrocardiogram image by combining binary classification (enhanced AdaBoost technique) and multi-class classification data gathered by connecting IoT sensor-based device in hand and blood profile levels. Our methodology shows different strategies by accomplishing strength even when expanding dataset size and error rate. It performs 88% exactness, and various measurements are AUC-0.88, a misclassification rate through which it was demonstrated that the Adabooster classifier beats other classifiers.
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Roy et al. (2026) studied Coronary artery disease. Enhanced AdaBoost machine learning technique vs. Other classifiers was evaluated on Classification accuracy of coronary artery disease (AUC 0.88). An enhanced AdaBoost machine learning technique for predicting coronary artery disease achieved 88% accuracy and an AUC of 0.88, outperforming other classifiers.
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