Skip to main content

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

Dubey, V. N., Dave, J.M., Beavis, J. and Coppini, D.V., 2020. Predicting diabetic neuropathy risk level using artificial neural network and clinical parameters of subjects with diabetes. Journal of Diabetes Science and Technology. (In Press)

Full text available as:

[img]
Preview
PDF
DUBEY Manuscript1_acceptedVersion.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

631kB

Official URL: https://journals.sagepub.com/toc/DST/0/0

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 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.

Item Type:Article
ISSN:1932-2968
Uncontrolled Keywords:diabetic neuropathy; neurothesiometer; vibration perception threshold; artificial neural network; VibraScan.
Group:Faculty of Science & Technology
ID Code:34599
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:28 Sep 2020 10:09
Last Modified:26 Oct 2020 11:14

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -