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

TL;DR

This paper develops a machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using data from the Michigan neuropathy screening instrument, providing a reliable and easy-to-use grading system for clinical management.

Contribution

It introduces a novel machine learning model that predicts DSPN severity levels from MNSI features, validated on multiple datasets, and creates a practical scoring system for clinical use.

Findings

Identified key features for DSPN using machine learning.

Achieved high AUC scores of 0.9421 and 0.946 for the model.

Developed a four-level severity scoring system.

Abstract

Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulceration and amputation. The Michigan neuropathy screening instrument (MNSI) is one of the most common screening techniques for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modelling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. MNSI variables and patient outcomes were investigated using machine learning tools to identify the features having higher association in DSPN identification. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading. Results: The top-7 ranked features from MNSI: 10-gm filament, Vibration perception (R), Vibration perception (L), previous diabetic neuropathy, the appearance of deformities, appearance of callus and appearance of fissure were identified as key features for 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 was predicted and a DSPN severity scoring system for MNSI was developed from the probability score. The model performance was validated on an independent dataset. 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 DSPN probability less than 50%, 75% to 90%, and above 90%, respectively. Conclusions: This study provides a simple, easy-to-use and reliable algorithm for defining the prognosis and management of patients with DSPN.