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Rahmani Katigari M, Ayatollahi H, Malek M, et al. Fuzzy expert system for diagnosing diabetic neuropathy. World J Diabetes. 2017;8(2):80-88doi: 10.4239/wjd.v8.i2.80.
Rahmani Katigari, M., Ayatollahi, H., Malek, M., & Kamkar Haghighi, M. (2017). Fuzzy expert system for diagnosing diabetic neuropathy. World journal of diabetes, 8(2), 80-88. https://doi.org/10.4239/wjd.v8.i2.80
Rahmani Katigari, Meysam, et al. "Fuzzy expert system for diagnosing diabetic neuropathy." World journal of diabetes vol. 8,2 (2017): 80-88. doi: https://doi.org/10.4239/wjd.v8.i2.80
Rahmani Katigari M, Ayatollahi H, Malek M, Kamkar Haghighi M. Fuzzy expert system for diagnosing diabetic neuropathy. World J Diabetes. 2017 Feb 15;8(2):80-88. doi: 10.4239/wjd.v8.i2.80. PMID: 28265346; PMCID: PMC5320751.
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World J Diabetes. 2017 Feb 15;8(2):80-88. doi: 10.4239/wjd.v8.i2.80.

Fuzzy expert system for diagnosing diabetic neuropathy.

World journal of diabetes

Meysam Rahmani Katigari, Haleh Ayatollahi, Mojtaba Malek, Mehran Kamkar Haghighi

Affiliations

  1. 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.

PMID: 28265346 PMCID: PMC5320751 DOI: 10.4239/wjd.v8.i2.80

Abstract

AIM: 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 (

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%).

CONCLUSION: 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.

Keywords: Artificial intelligence; Diabetes complications; Diabetes mellitus; Diabetic neuropathies; Expert systems; Fuzzy logic

Conflict of interest statement

Conflict-of-interest statement: There are no conflicts of interest arising from this work.

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