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A machine learning-based severity prediction tool for the Michigan neuropathy screening instrument

Haque, Fahmida and Reaz, Mamun B. I. and Chowdhury, Muhammad E. H. and Shapiai, Mohd. Ibrahim and A. Malik, Rayaz and Mohammed Alhatou, Mohammed Alhatou and Kobashi, Syoji and Iffat Ara, Iffat Ara and M. Ali, Sawal H. and A. Bakar, Ahmad A. and Bhuiyan, Mohammad Arif Sobhan (2023) A machine learning-based severity prediction tool for the Michigan neuropathy screening instrument. Diagnostics, 13 (2). pp. 1-16. ISSN 2075-4418

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Official URL: http://dx.doi.org/10.3390/diagnostics13020264

Abstract

Diabetic 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.

Item Type:Article
Uncontrolled Keywords:DSPN, machine learning, MNSI, nomogram, severity grading
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:106560
Deposited By: Widya Wahid
Deposited On:09 Jul 2024 06:56
Last Modified:09 Jul 2024 06:56

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