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

Ahmad Shaker Abdalrada; Jemal Abawajy; Tahsien Al-Quraishi; Sheikh Mohammed Shariful Islam

doi:10.1177/20420188221086693

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

Ther Adv Endocrinol Metab. 2022 Mar 22:13:20420188221086693. doi: 10.1177/20420188221086693. eCollection 2022.

Authors

Ahmad Shaker Abdalrada¹, Jemal Abawajy², Tahsien Al-Quraishi¹, Sheikh Mohammed Shariful Islam³

Affiliations

¹ Faculty of Computer Science and Information Technology, Wasit University, Al Kut, Iraq.
² School of Information Technology, Deakin University, Melbourne, VIC, Australia.
³ Institute for Physical Activity and Nutrition, Deakin University, 221 Burwood Highway, Burwood, Melbourne, VIC 3125, Australia.

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.

Keywords: Ewing’s battery tests; cardiac autonomic neuropathy (CAN); diabetes mellitus (DM); health informatics; logistic regression; predictive analysis.