Researchers from Department of Mechanical Engineering Publish New Studies and Findings in the Area of Digital Health (Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction).

A recent study published in the journal Frontiers in Digital Health discusses the development of a new algorithm, called the HDBN-XG algorithm, for predicting coronary heart disease. The algorithm analyzes key physiological data, such as Electrocardiogram (ECG) readings and blood volume measurements, and uses a multi-objective artificial bee colony approach to construct feature subsets. The researchers found that the HDBN-XG algorithm achieved high accuracy, precision, specificity, sensitivity, and F1-measure, outperforming existing classifiers. The study contributes to predictive analytics in healthcare and aims to mitigate the global impact of coronary heart disease. [Extracted from the article]