Krzysztof Irlik, Hanadi Aldosari, Mirela Hendel, Hanna Kwiendacz, Julia Piaśnik, Justyna Kulpa, Paweł Ignacy, Sylwia Boczek, Mikołaj Herba, Kamil Kegler, Frans Coenen, Janusz Gumprecht, Yalin Zheng, Gregory Y H Lip, Uazman Alam, Katarzyna Nabrdalik
AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). MATERIALS AND METHODS: We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC)...
April 11, 2024: Diabetes, Obesity & Metabolism