Performance of a fuzzy set theoretic model for medical diagnosis
Augustine O. Esogbue · 2005
Classical mathematical models for medical diagnosis which have been computerized are known to perform very poorly when compared to diagnoses made by the physician. Factors which contribute to their poor performance relate to the omission by these models of important information on the patient such as symptoms of past undiagnosed diseases which can only be vaguely recalled by the patient. Other deficiencies include failure to model the stage of development of the disease and certain intrinsically fuzzy aspects of the pertinent information nets that are needed to develop a medical hypothesis. Models which attempted to remedy these shortcomings were developed and presented by the authors elsewhere. In this effort, we describe a study in which our fuzzy diagnosis models were computerized, validated and compared with a mock physician hypothesis as well as existing mathematical models. The example Involved a medical hypothsis concerning a medical condition of valvular heart disease. The results show that while there were discrepancies between the fuzzy model's and the physician's hypotheses, the model's performance was vastly superior to that of existing mathematical models.