Predicting Diabetic Neuropathy Risk Level Using Artificial Neural Network and Clinical Parameters of Subjects With Diabetes.

Authors: Dubey, V.N., Dave, J.M., Beavis, J. and Coppini, D.V.

Journal: J Diabetes Sci Technol

Volume: 16

Issue: 2

Pages: 275-281

eISSN: 1932-2968

DOI: 10.1177/1932296820965583

Abstract:

BACKGROUND: A risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy, which utilizes vibration perception threshold (VPT) and a set of clinical variables as potential predictors. METHODS: Significant risk factors included age, height, weight, urine albumin-to-creatinine ratio, glycated hemoglobin, total cholesterol, and duration of diabetes. The continuous-scale VPT was recorded using a neurothesiometer and classified into three categories based on the clinical thresholds in volts (V): low risk (0-20.99 V), medium risk (21-30.99 V), and high risk (≥31 V). RESULTS: The initial study had shown that by just using patient data (n = 5088) an accuracy of 54% was achievable. Having established the effectiveness of the "classical" method, a special Neural Network based on a Proportional Odds Model was developed, which provided the highest level of prediction accuracy (>70%) using the simulated patient data (n = 4158). CONCLUSION: In the absence of any assessment devices or trained personnel, it is possible to establish with reasonable accuracy a diagnosis of diabetic neuropathy by means of the clinical parameters of the patient alone.

http://eprints.bournemouth.ac.uk/34599/

Source: PubMed

Predicting diabetic neuropathy risk level using artificial neural network and clinical parameters of subjects with diabetes

Authors: Dubey, V., Dave, J., Beavis, J. and Coppini, D.

Journal: Journal of Diabetes Science and Technology

Publisher: Diabetes Technology Society

ISSN: 1932-2968

Abstract:

Background: A risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy which utilises vibration perception threshold (VPT) and a set of clinical variables as potential predictors. Methods: Significant risk factors included: age, height, weight, urine albumin to creatinine ratio (ACR), HbA1c, total cholesterol and duration of diabetes. The continuous scale VPT was recorded using a Neurothesiometer and classified into three categories based on the clinical thresholds in volts (V): low risk (0 to 20.99 V), medium risk (21 to 30.99 V) and high risk (≥ 31 V). Results: The initial study had shown that by just using patient data (n=5088) an accuracy of 54% was achievable. Having established the effectiveness of the “classical” method a special Neural Network based Proportional Odds Model (NNPOM) was developed which provided the highest level of prediction accuracy (>70%) using the simulated patient data (n=4158).

Conclusion: In the absence of any assessment devices or trained personnel it is possible to establish with reasonable accuracy a diagnosis of diabetic neuropathy by means of the clinical parameters of the patient alone.

http://eprints.bournemouth.ac.uk/34599/

Source: Manual