Diagnosis diabetes on the basis of information extracted from the ECG signal using Artificial Neural Networks

Background and aims: Diabetes is known to be one of the most common diseases worldwide. Lack of an early and appropriate diagnosis is considered to be a main problem associated with diabetes. The aim of this study was to offer a novel approach to diagnose diabetes and, for the first time, investigates the correlation between ECG and diagnosis of diabetes using artificial neural network and data analysis algorithms. Methods: In this study, 8 patients with diabetes and 64 healthy subjects were enrolled. ECG was conducted on all the participants. The necessary data including age, HR, p, t, RR, PP, P, PR, qt, and qtcb were drawn from ECG and collected in database. To classify the patients, tentative neural networks and standard algorithms were used. The data were analyzed using data analysis algorithms and different approaches, and the results of each investigation were compared with reference to appropriate rate. Weka software was used for ranking. Results: The accuracy of detection of regulations-based algorithms and neural network, with better results in diabetes diagnosis, was higher than that of decision tree and interval-based algorithms. The best qualification rate (0.89) was obtained for ConsistencySubset Eval and QRS wave was reported the best choice in all algorithms. Investigating the data on people with and without diabetes using tentative neural networks showed an appropriate rate of 95%. Furthermore, KNN algorithm displayed the lowest time complexity. Conclusion: Regulations-based model displayed the highest accuracy compared with all classification algorithms for data analysis used in the study