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AuthorHaque, Fahmida
AuthorReaz, Mamun B. I.
AuthorChowdhury, Muhammad E. H.
AuthorShapiai, Mohd I.
AuthorMalik, Rayaz A.
AuthorAlhatou, Mohammed
AuthorKobashi, Syoji
AuthorAra, Iffat
AuthorAli, Sawal H. M.
AuthorBakar, Ahmad A. A.
AuthorBhuiyan, Mohammad A.
Available date2023-04-17T06:57:41Z
Publication Date2023
Publication NameDiagnostics
ResourceScopus
URIhttp://dx.doi.org/10.3390/diagnostics13020264
URIhttp://hdl.handle.net/10576/41930
AbstractDiabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram's area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model's performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN. 2023 by the authors.
SponsorThis work was made possible by the Qatar National Research Fund (QNRF) NPRP12S-0227-190164, the International Research Collaboration Co-Fund (IRCC) grant, Grant Number: IRCC-2021-001 and Universiti Kebangsaan Malaysia (UKM) Grant Number DIP-2020-004 and Grant Number UKM-TR-011. Open Access publication of this article is supported by the Qatar National Library. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherMDPI
SubjectDSPN
machine learning
MNSI
nomogram
severity grading
TitleA Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument
TypeArticle
Issue Number2
Volume Number13
dc.accessType Open Access


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