title: Fuzzy expert system for diagnosing diabetic neuropathy.
abstract: OBJECTIVE
  To design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy.
  
  
  METHODS
  The 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.
  
  
  RESULTS
  The 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%).
  
  
  CONCLUSIONS
  The 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.
authors.affiliation: Meysam Rahmani Katigari, Haleh Ayatollahi, Mehran Kamkar Haghighi, Department of Health Information Management, School of Health Management and Information Sciences, IRAN University of Medical Sciences, Tehran 1996713883, Iran.
authors.forename: Meysam
authors.initials: M
authors.lastname: Rahmani Katigari
authors.affiliation: Meysam Rahmani Katigari, Haleh Ayatollahi, Mehran Kamkar Haghighi, Department of Health Information Management, School of Health Management and Information Sciences, IRAN University of Medical Sciences, Tehran 1996713883, Iran.
authors.forename: Haleh
authors.initials: H
authors.lastname: Ayatollahi
authors.affiliation: Meysam Rahmani Katigari, Haleh Ayatollahi, Mehran Kamkar Haghighi, Department of Health Information Management, School of Health Management and Information Sciences, IRAN University of Medical Sciences, Tehran 1996713883, Iran.
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Malek
authors.affiliation: Meysam Rahmani Katigari, Haleh Ayatollahi, Mehran Kamkar Haghighi, Department of Health Information Management, School of Health Management and Information Sciences, IRAN University of Medical Sciences, Tehran 1996713883, Iran.
authors.forename: Mehran
authors.initials: M
authors.lastname: Kamkar Haghighi
keywords: Artificial intelligence; Diabetes complications; Diabetes mellitus; Diabetic neuropathies; Expert systems; Fuzzy logic