Identification of Cardiovascular Diseases Risk Factors among Diabetes Patients Using Ontological Data Mining Techniques

Healthcare environment is rich of data, but still needs knowledge extraction that is necessarily important for saving people lives. Medical Knowledge discovery is a process of extracting knowledge patterns from biomedical data, which is useful and crucial for making effective decisions especially in developing strategies and policies of preventive medical treatments. Data mining methods are the best-known way to recognize the hidden data standards. Ontology engineering used to improve knowledge domain representation, and further is considered for the enhancement and refinement of the mining techniques based on the discovered patterns driven from ontological data mining. In this paper, we apply ontology driven data mining techniques on a data set of diabetes patients who have cardiovascular disease. That process performed to identify the relationship between type two diabetes mellitus patients and their important laboratory tests specified by doctors. Doctors aim to investigate the probability of cardiovascular disease occurrence and stroke happening. Ontology driven Data mining techniques also used in experimental study as well as rule induction, association rules methods. In a late phase, we used frequent pattern discovery and rules induction method using ontological data mining algorithm (RMonto). The findings of this study reveals that the use of ontologies minimizes the number of attributes in the preprocessing stage and helps in all data mining stages; in addition to its important role in ontological data mining, we have a higher learning accuracy ratio exceeding 90%. The results of data mining methods and ontological data mining shows that the significance of some laboratory tests like: LP(a),CRP,HDL,FBG,TG,LDH and Chol to predict CVD risk among T2DM patients with a high accuracy.