Evidence map›Paper›PMID 28265346›Full record

ArticleWorld journal of diabetes2017

Fuzzy expert system for diagnosing diabetic neuropathy.

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

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Article in World journal of diabetes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Meysam Rahmani KatigariMeysam 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.
Haleh AyatollahiMeysam 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 MalekMeysam 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.
Mehran Kamkar HaghighiMeysam 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

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.

Indexed as

Artificial intelligenceDiabetes complicationsDiabetes mellitusDiabetic neuropathiesExpert systemsFuzzy logic

Identifiers

PMID28265346
PMCPMC5320751

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