Prediction of cardiac autonomic neuropathy using a machine learning model in patients with diabetes.

Abstract

Background: Cardiac autonomic neuropathy (CAN) is a diabetes-related complication with increasing prevalence and remains challenging to detect in clinical settings. Machine learning (ML) approaches have the potential to predict CAN using clinical data. In this study, we aimed to develop and evaluate the performance of an ML model to predict early CAN occurrence in patients with diabetes.

Methods: We used the diabetes complications screening research initiative data set containing 200 CAN-related tests on more than 2000 participants with type 2 diabetes in Australia. Data were collected on peripheral nerve functions, Ewing's tests, blood biochemistry, demographics, and medical history. The ML model was validated using 10-fold cross-validation, of which 90% were used in training the model and the remaining 10% was used in evaluating the performance of the model. Predictive accuracy was assessed by area under the receiver operating curve, and sensitivity, specificity, positive predictive value, and negative predictive value.

Results: Of the 237 patients included, 105 were diagnosed with an early stage of CAN while the remaining 132 were healthy. The ML model showed outstanding performance for CAN prediction with receiver operating characteristic curve of 0.962 [95% confidence interval (CI) = 0.939-0.984], 87.34% accuracy, and 87.12% sensitivity. There was a significant and positive association between the ML model and CAN occurrence (p < 0.001).

Conclusion: Our ML model has the potential to detect CAN at an early stage using Ewing's tests. This model might be useful for healthcare providers for predicting the occurrence of CAN in patients with diabetes, monitoring the progression, and providing timely intervention.