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Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-termcomplication in diabetic patients associated with painful neuropathy, footulceration and amputation. The Michigan neuropathy screening instrument (MNSI)is one of the most common screening techniques for DSPN, however, it does notprovide any direct severity grading system. Method: For designing and modellingthe DSPN severity grading systems for MNSI, 19 years of data from Epidemiologyof Diabetes Interventions and Complications (EDIC) clinical trials were used.MNSI variables and patient outcomes were investigated using machine learningtools to identify the features having higher association in DSPNidentification. A multivariable logistic regression-based nomogram wasgenerated and validated for DSPN severity grading. Results: The top-7 rankedfeatures from MNSI: 10-gm filament, Vibration perception (R), Vibrationperception (L), previous diabetic neuropathy, the appearance of deformities,appearance of callus and appearance of fissure were identified as key featuresfor identifying DSPN using the extra tree model. The area under the curve (AUC)of the nomogram for the internal and external datasets were 0.9421 and 0.946,respectively. From the developed nomogram, the probability of having DSPN waspredicted and a DSPN severity scoring system for MNSI was developed from theprobability score. The model performance was validated on an independentdataset. Patients were stratified into four severity levels: absent, mild,moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPNprobability less than 50%, 75% to 90%, and above 90%, respectively.Conclusions: This study provides a simple, easy-to-use and reliable algorithmfor defining the prognosis and management of patients with DSPN.

A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations