aspg-28-1979-1792155076 20261016125116 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2023 4 2 Abdelaziz A. Abdelhamid Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt Mostafa Abotaleb Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia S. K. Towfek Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Diabetes patients face a severe health cost from cardiovascular disease (CVD). Recognising the risk factors for CVD in this group of people is critical for developing effective preventative and management measures. In this study, we use an ontological data mining approach, LightGBM, to analyze a dataset of diabetes patients and investigate the risk variables that contribute to CVD. The association between diabetes and CVD is investigated, emphasising the increased risk that diabetes patients confront. We look into the demographics, health behaviors, and physiological indicators that influence the emergence of heart disease in this population. We use LightGBM to find complicated relationships and trends within the dataset, allowing us to identify critical risk variables. Our research contributes to the field by offering a thorough examination of the diabetes-CVD link and applying an advanced machine-learning technique for information extraction. The results have implications for specific interventions, risk evaluation models, and personalised therapy approaches aimed at reducing the effect of CVD in diabetics. 2023 45 53 1979 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.040205 https://www.americaspg.com/journal/28/article/1979 https://www.americaspg.com/storage/71691333957.pdf