Abstract
This article proposes an inference module for an Expert System for diabetes diagnosis. This module is based on a Bayesian Network (BN), which represents knowledge, experience and reasoning mechanisms of a specialist in family medicine of the Mexican Social Security Institute (IMSS). The events and causal relations of the Bayesian Network are obtained from the variables or symptoms of four types of diabetes: Diabetes Mellitus Type I (DMI), Diabetes Mellitus Type II (DMII), Gestational Diabetes (GD) and Prediabetes (PD) or Insulin Resistance. The evidences to build the Bayesian Network were obtained with a first version of a preliminary Expert System (ES) for diabetes; these evidences correspond to a set of 250 selected patients. We present interesting results obtained with this new inference module by comparing both results, those obtained with the preliminary ES and those obtained with the new proposal.
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Cruz-Gutiérrez, V., Posada-Zamora, M.A., Sánchez-López, A. (2017). An Efficient Expert System for Diabetes with a Bayesian Inference Engine. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_5
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DOI: https://doi.org/10.1007/978-3-319-62428-0_5
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