Application of the dempster-shafer method in medical expert systems for disease diagnosis
Keywords:
Bayesian Evidance, Dempster-Shafer, Expert System, Medical Diagnosis, UncertaintyAbstract
The use of expert systems in medical field allows computers to replicate human diagnostic reasoning. Among the available uncertainty-based approaches, Dempster-shafer theory of evidance provides a powerful probabilistic framework capable of combining incomplete and imprecise information for decision support. This article synthesizes three applied studies that employed the dempster-shafer method to diagnose Idiopathic Thrombocytopenic Purpura (ITP), Encephalitis and Periodontal Disease. Each implementation demonstrated that the Dempster-Shafer algorithm could quantify belief level in uncertain clinical evidance such as symptoms and laboratory results. Through successive evidance combination, the the systems achived diagnostic comfidence levels between 92%-97% across cases. Collectively these finding reaffirm that Dempster-Shafer reasoning can enhance diagnostic reliability compared to deterministic or rule-based systems by explicitly managing uncertainty and integrating multi-source medical data.
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