Cardiac Condition Anticipation and Prognostication via Integrated WOA and Bagging-GBDT - Venkatarao, Javvaji; Chakravarthy, V. Deeban (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Cardiac disease is a significant health concern that leads to more than 17 million deaths every year. The integration of Internet of Medical Things (IoMT) and Artificial Intelligence (AI) in healthcare has made considerable improvements in patient outcomes. However, the recent approach of using the Bagging-Fuzzy-GBDT classifier for anticipating and prognosticating Cardiac disease may not be suitable for all cases due to membership function-based data fuzzification and Grid Search (GS) based hyperparameter selection limitations. This study presents a novel method combining optimization with ensemble learning techniques, specifically Whale Optimization Algorithm (WOA), with Bagging-GBDT classifiers to anticipate and prognosticate Cardiac disease more effectively. This new approach employs membership functions to capture data uncertainty and vagueness, uses ensemble learning techniques to generate multiple random subsamples from the original dataset, and finally utilizes the Bagging-WOA-GBDT classifier to build an accurate prognostication model based on enhanced data representation. The results of the experiment conducted on a publicly available Cardiac disease dataset show that the proposed approach performs better than traditional classifier methods. It provides more reliable and accurate prognostication for Cardiac disease. These findings suggest that the suggested approach could be a valuable tool for healthcare practitioners in diagnosing and preventing Cardiac disease.