Performance of Bayesian networks (naive Bayes and tree augmented naive Bayes) in detecting diabetes types 1 and 2 (Journal of Pure and Applied Sciences (Science Forum), 2026)
This study is aimed at studying diabetes data using Bayesian networks and evaluating their performance in detecting types 1 and 2 diabetes. The diabetes dataset was obtained from the medical records unit of the Abubakar Tafawa Balewa University Teaching Hospital Bauchi, Bauchi State, consisting of 569 cases with 8 different variables. Classification of diabetes patients was carried out by naive Bayes and tree augmented naive Bayes (TAN) networks; the networks were trained and tested using a 10-fold cross-validation and the quality of prediction of these networks in terms of sensitivity, specificity, area under the ROC (AUR), kappa statistic, mean absolute error (MAE), and root mean square error (RMSE) was evaluated. The results indicate that TAN network proved to be the better network because the method correctly classified 530 (93.15%) and misclassified only 39 (6.85%) patients; it also has higher AUR (0.949) and kappa (0.7869), as well as lower MAE and RMSE of 0.1032 and 0.2384, respectively.