Comparison of Bayesian Networks for Diabetes Prediction

  • Kanogkan LeerojanaprapaEmail author
  • Kittiwat Sirikasemsuk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 924)


A Bayesian network (BN) can be used to predict the prevalence of diabetes from the cause–effect relationship among risk factors. By applying a BN model, we can capture the interdependencies between direct and indirect risks hierarchically. In this study, we propose to investigate and compare the predictive performances of BN models with non-hierarchical (BNNH), and non-hierarchical and reduced variables (BNNHR) structures, hierarchical structure by expert judgment (BNHE), and hierarchical learning structure (BNHL) with type-2 diabetes. ROC curves, AUC, percentage error, and F1 score were applied to compare performances of those classification techniques. The results of the model comparison from both datasets (training and testing) obtained from the Thai National Health Examination Survey IV ensured that BNHE can predict the prevalence of diabetes most effectively with the highest AUC values of 0.7670 and 0.7760 from the training and the testing dataset, respectively.


Bayesian network Diabetes Machine learning 



This research is supported by King Mongkut’s Institute of Technology Ladkrabang [No. 2559-02-05-050]. We would like to thank the Thai Public Health Survey Institute for Health Systems Research for providing helpful datasets.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kanogkan Leerojanaprapa
    • 1
    Email author
  • Kittiwat Sirikasemsuk
    • 2
  1. 1.Department of Statistics, Faculty of ScienceKing Mongkut’s Institute of Technology Ladkrabang (KMITL)BangkokThailand
  2. 2.Department of Industrial Engineering, Faculty of EngineeringKing Mongkut’s Institute of Technology Ladkrabang (KMITL)BangkokThailand

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