From Evaluation to Prediction: Analysis of Diabetic Autonomic Neuropathy Using Sudoscan and Artificial Intelligence

Abstract

A dangerous side effect of diabetes that can significantly lower quality of life and raise the death rate of diabetic individuals is diabetic autonomic neuropathy. It is essential to identify and anticipate this disease early on for prompt intervention and care. This study aims to predict this diabetic complication using Sudoscan and artificial intelligence. In this study, 172 individuals with type 1 or type 2 diabetes mellitus provided clinical and demographic information. Sudoscan was used to evaluate the subjects’ sudomotor dysfunction. Statistical methods were used to link various electrochemical skin conductance values with risk factors for neuropathy such as age, BMI, age of diabetes, or biochemical values such as cholesterol and triglycerides. Different machine-learning algorithms were used to predict the risk of diabetic autonomic neuropathy based on the collected data. The accuracy achieved with Logistic Regression is 92.6%, and with the Random Forest model is 96.3%. Lazzy Classifiers also show that six classifiers have a high performance of 97%. Thus, the use of machine learning algorithms in this field of metabolic diseases offers new perceptions for diagnosis, treatment, and prevention, and improves the quality of life of diabetic patients by reducing the incidence of complications related to this disease.