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Article

Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques

1
Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea
2
Deargen, Co., Ltd., Daejeon 34051, Korea
3
Department of Endocrinology and Metabolism, College of Medicine, Dankook University, Cheonan 31116, Korea
4
Department of Laboratory Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea
5
Department of Nanobiomedical Science & BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Korea
6
Institute of Tissue Regeneration Engineering (ITREN), Dankook University, Cheonan 31116, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Fernando Gómez-Peralta
J. Clin. Med. 2021, 10(19), 4576; https://doi.org/10.3390/jcm10194576
Received: 7 August 2021 / Revised: 28 September 2021 / Accepted: 30 September 2021 / Published: 2 October 2021
(This article belongs to the Special Issue Clinical Research on Type 2 Diabetes and Its Complications)
Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN. View Full-Text
Keywords: machine learning; diabetes mellitus; diabetic sensorimotor polyneuropathy; random forest; prediction; electrophysiology machine learning; diabetes mellitus; diabetic sensorimotor polyneuropathy; random forest; prediction; electrophysiology
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MDPI and ACS Style

Shin, D.Y.; Lee, B.; Yoo, W.S.; Park, J.W.; Hyun, J.K. Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques. J. Clin. Med. 2021, 10, 4576. https://doi.org/10.3390/jcm10194576

AMA Style

Shin DY, Lee B, Yoo WS, Park JW, Hyun JK. Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques. Journal of Clinical Medicine. 2021; 10(19):4576. https://doi.org/10.3390/jcm10194576

Chicago/Turabian Style

Shin, Dae Y., Bora Lee, Won S. Yoo, Joo W. Park, and Jung K. Hyun. 2021. "Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques" Journal of Clinical Medicine 10, no. 19: 4576. https://doi.org/10.3390/jcm10194576

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