A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument
Fahmida Haque,
Mamun B. I. Reaz,
Muhammad E. H. Chowdhury,
Mohd Ibrahim bin Shapiai,
Rayaz A. Malik,
Mohammed Alhatou,
Syoji Kobashi,
Iffat Ara,
Sawal H. M. Ali,
Ahmad A. A. Bakar,
Mohammad Arif Sobhan Bhuiyan
Affiliations
Fahmida Haque
Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Mamun B. I. Reaz
Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Muhammad E. H. Chowdhury
Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
Mohd Ibrahim bin Shapiai
Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
Rayaz A. Malik
Department of Medicine, Weill Cornell Medicine—Qatar, Doha 24144, Qatar
Mohammed Alhatou
Neuromuscular Division, Hamad General Hospital, Doha 3050, Qatar
Syoji Kobashi
Graduate School of Engineering, University of Hyogo, Himeji 678-1297, Hyogo, Japan
Iffat Ara
Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
Sawal H. M. Ali
Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Ahmad A. A. Bakar
Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Mohammad Arif Sobhan Bhuiyan
Electrical and Electronics Engineering, Xiamen University Malaysia, Sepang 43900, Malaysia
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.
