Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 4 , Issue 1 , PP: 08-15, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Hussein Alkattan 1 * , S. K. Towfek 2 , M. Y. Shams 3
Doi: https://doi.org/10.54216/JAIM.040101
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.
Ontological Data Mining , Cardiovascular Disease , Diabetes , Boosting , Predictive Models , Interpretability , Data Visualization.
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