Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3992 2018 2018 Applied College , King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia Mohammed Mohammed Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia Nesren Farhah VIT Bhopal University, Bhopal, India Rajit Nair Applied College , King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia Mohammed Awad Mohammed Ataelfadiel Applied College , King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia; Vice-Presidency for Postgraduate Studies and Scientific Research, King Faisal University, Al-Ahsa 31982, Saudi Arabia Rami Taha shehab Heart disease is a severe hazard to the public's health and safety because of the high rates of disability and mortality it causes. Accurate disease prediction and diagnosis are more critical than ever in this era of earlier illness prevention, faster disease detection, and earlier disease treatment. Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) have made it possible to detect, forecast, and diagnose cardiovascular disease more precisely. However, the bulk of these prediction models can only state whether a person is sick; they cannot and do not forecast the severity of the ailment. We present a machine-learning-based technique for predicting cardiovascular disease. Using this strategy, we hope to perform binary and multimodal classifications at the same time. To get things started, we will go through the fuzzy-adaboost approach, which will serve as the foundation for the rest of our work. By combining fuzzy logic and the Adaboost method, this method aims to increase the number of applications that can use binary classification prediction to simplify data analysis. If it is completed, both objectives will be met, and we will eliminate overfitting by merging bagging and fuzzy adaboost into a single approach. It is the ideal solution to the challenge we are currently facing. Because it has a separate classification for the severity of the presentation of heart disease, the bagging fuzzy adaboost can be used for multiclassification prediction. This is because Adaboost's assessment of the severity of the observed heart disease presentations is unclear and imprecise. The results of the experiment reveal that, in addition to a wide range of other classes, the Bagging-Fuzzy-Adaboost can anticipate binary data accurately. When compared to traditional procedures, it is evident that this has significant advantages. 2026 2026 293 306 10.54216/FPA.210121 https://www.americaspg.com/articleinfo/3/show/3992