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PubMed ID:
34715551
Public Release Type:
Journal
Publication Year: 2021
Affiliation: epartment of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia b Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar c Graduate School of Engineering, University of Hyogo, Hyogo, Japan d P. R. Pote College of Engineering and Management, Kathora Road, Amravati, 444602, India e Department Electrical and Electronic Engineering, Xiamen University Malaysia, Bandar Sunsuria, Sepang, 43900, Selangor, Malaysia
DOI:
https://doi.org/10.1016/j.compbiomed.2021.104954
Authors:
Haque F,
Bin Ibne Reaz M,
Chowdhury MEH,
Hamid Md Ali S,
Ashrif A Bakar A,
Rahman T,
Kobashi S,
Dhawale CA,
Arif Sobhan Bhuiyan M
Request IDs:
23125
Studies:
Diabetes Prevention Program Outcomes Study
Background: Diabetic Sensorimotor polyneuropathy (DSPN) is one of the major indelible complications in diabetic patients. Michigan neuropathy screening instrumentation (MNSI) is one of the most common screening techniques used for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features. Results: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%–90%, and above 90%, respectively. Conclusions: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.
