Evidence-Based Medicine
Copyright ©The Author(s) 2017. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Feb 15, 2017; 8(2): 80-88
Published online Feb 15, 2017. doi: 10.4239/wjd.v8.i2.80
Fuzzy expert system for diagnosing diabetic neuropathy
Meysam Rahmani Katigari, Haleh Ayatollahi, Mojtaba Malek, Mehran Kamkar Haghighi
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
Mojtaba Malek, Division of Endocrinology and Metabolism, Research Center of Firoozgar Hospital, IRAN University of Medical Sciences, Tehran 1593747811, Iran
Author contributions: All authors contributed to this manuscript.
Supported by The Iran University of Medical Sciences, No. 541.
Conflict-of-interest statement: There are no conflicts of interest arising from this work.
Data sharing statement: No further data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Haleh Ayatollahi, Assistant Professor in Medical Informatics, Department of Health Information Management, School of Health Management and Information Sciences, IRAN University of Medical Sciences, No. 6, Shahid Yasami St., Vali-e-Asr St., Tehran 1996713883, Iran. ayatollahi.h@iums.ac.ir
Telephone: +98-21-88794301 Fax: +98-21-88794301
Received: July 27, 2016
Peer-review started: July 29, 2016
First decision: September 8, 2016
Revised: October 12, 2016
Accepted: December 1, 2016
Article in press: December 2, 2016
Published online: February 15, 2017
Processing time: 202 Days and 15.7 Hours
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 (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%).

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

Core tip: In this study, an expert system was designed for diagnosing diabetic neuropathy. This system can help specialists to diagnose the disease more quickly by using the most common diagnostic parameters. Even general practitioners can use this system in remote areas to improve the quality of care for patients with diabetes. With it, patients will no longer need to undertake complex procedures, and the care plan can be applied at the right time.