Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/1939 2022 2022 Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia Hussein Alkattan Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA S. K. Towfek Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt M. Y. Shams The prevalence of cardiovascular disease (CVD) is a serious public health issue, and it is of particular concern for people with diabetes because of the increased risk of cardiovascular problems that these people experience. In this study, we suggest a novel method of Ontological Data Mining (ODM) for identifying the origins of CVD risk in diabetic patients. We want to improve the readability and precision of prediction models by incorporating domain knowledge and semantic linkages into the data mining process. In this work, we examine a large dataset consisting of 70,000 patient records with 11 attributes, all of which are derived through a thorough clinical history and physical examination. As part of our methodology, we use decision trees, support vector machines (SVMs), and gradient boosting (GB). The distribution patterns of critical variables with respect to CVD outcomes can be better understood through the use of visual representations such as box plots, distributional plots, and pie charts. Finding significant connections and causal relationships between risk factors and CVD outcomes is made possible by the suggested ODM method. Our research has promising implications for bettering the treatment of patients with diabetes, facilitating targeted interventions, and enhancing risk assessment and preventative methods for cardiovascular disease. 2023 2023 08 15 10.54216/JAIM.040101 https://www.americaspg.com/articleinfo/28/show/1939