Learning Diabetes Data Using Bayesian Network and Examining the Factors that Contribute to Development of Diabetes Types I and II

Bayesian networks (BN) are an excellent tool for classification of a complex inter-correlated data such as medical data. This study is aimed at learning diabetes data using CI Search Algorithm and to determine the factors that cause Type 1 and Type 2 Diabetes. Diabetes dataset was obtained from medical records unit of the Abubakar Tafawa Balewa University Teaching Hospital (ATBUTH) Bauchi, Bauchi State consisting of 569 cases with 8 different variables. To achieved the stated objectives, Bayesian Network (BN) were used i.e. CI Search Algorithm. CI Search Algorithm explored the nature of relationship between the attributes and diabetes type (T1D/T2D). Conditional Probability Tables (CPT) obtained by CI Search Algorithm reveals that a diabetes patient of age between 45.5-72 years is 86% likely to be T2D, a diabetes patient of BMI between 24.6-38.6 is 89% likely to be T2D, a diabetes patient with history of diabetes is 64% likely to be T1D, a diabetes patient without history of diabetes is 66% likely to be T2D.