Fuzzy expert system for diagnosing diabetic neuropathy

Abstract

AIMTo design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy.METHODSThe research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists’ perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system.RESULTSThe final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%).CONCLUSIONThe system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

“…Out of the 75 papers we analysed, 26 reported the adoption of knowledge-based AI methods 60 – 85 , possibly in combination with data- driven methods 61 , 63 , 70 , 71 , 74 , 76 . This later group of 6 hybrid approaches will be specifically detailed in the next subsection.…”

“…The knowledge-based methods explicitly represent knowledge as symbols in the form of rules, ontologies, past cases, or other types of knowledge structures. Specifically, the surveyed approaches ranged from the use of ontologies 61 , 62 , 67 – 69 , to rule-based reasoning (including the adoption of fuzzy rules) 64 , 66 – 68 , 72 – 75 , 78 – 80 , 82 – 85 , to the definition of semantic or associative networks 76 , 77 , to temporal reasoning 65 , to probabilistic models such as Bayesian Networks 60 , 63 , 70 , 81 , and to case base reasoning CBR 61 , 71 .…”

“…As regards to the addressed task, most works deal with diagnosis 60 , 62 , 64 , 70 , 73 , 74 , 77 – 80 , 82 , 84 , 85 or therapy/treatment support 61 – 63 , 66 , 75 , 81 , while some works address classification 83 , patient’s behaviour tracking or activity scheduling 65 , and training 68 . A set of papers deals with computer-interpretable guidelines (CIGs) 67 , 69 , 71 , 72 .…”

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“…Machine learning approaches help physicians customize diabetes medications to optimize adherence and health outcomes. 34 AI-powered devices help physicians diagnose diabetes noninvasively, 35 and more accurately measure and monitor the severity of diabetic neuropathy 36 and diabetic wounds. 37…”

Ai Based Expert System for Prediction of Diabetic Eye Morbidity

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