An intelligent clustering based methodology for confusable diseases diagnosis and monitoring
The combination of non-specific clinical manifestations that characterize confusable tropical disease and the probable lack of expertise and experience among physicians exponentially increases the potential for misdiagnosis and subsequent increased morbidity and mortality rates resulting from these diseases. In this paper, an intelligent system driven by fuzzy clustering algorithm and Adaptive Neuro-Fuzzy Inference System for the investigation, diagnosis and management of similar and confusing symptoms of confusable diseases was developed. Data on patients diagnosed and confirmed by laboratory tests of viral hepatitis (H), malaria (M), typhoid fever (T) and urinary tract infection (U) were used for training, testing and validation of the system. The system assigns patients with severity levels in all the clusters. Results on clusters validity are satisfactory. Overlapping symptoms analysis shows that symptoms of both H and T have highest degree of overlapping while symptoms common to M and U yielded the least impact. Symptoms common to M, H and T only, have equal impact with that of M, T and U only. The symptoms that are common to all the four diseases under study yielded a 12.8% contribution to the degree of severity of each of the CTD diseases. The system compares favorably with diagnosis arrived at by experienced physicians and also provides patients‟ level of severity in each confusable disease and the degree of confusability of any two or more confusable diseases.
Keywords: Confusable Diseases; Viral Hepatitis; Malaria; Typhoid Fever; Urinary Tract Infection; Clustering, ANFIS.