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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 11, 2022.
Abstract: Diabetes is one of the most chronic diseases, with an increasing number of sufferers yearly. It can lead to several serious complications, including diabetic peripheral neuropathy (DPN). DPN must be recognized early to receive appropriate treatment and prevent disease exacerbation. However, due to the rapid development of machine learning classification, like in the health science sector, it is very easy to identify DPN in the early stages. Therefore, the aim of this study is to develop a new method for detecting neuropathy based on the myoelectric signal among diabetes patients at a low cost with utilizing one of the machine learning techniques, the artificial neural network (ANN). To that aim, muscle sensor V3 is used to record the activity of the anterior tibialis muscle. Then, the representative time domain features which is mean absolute value (MAV), root mean square (RMS), variance (VAR), and standard deviation (SD) used to evaluate fatigue. During neural network training, a different number of hidden neurons were used, and it was found that using seven hidden neurons showed a high accuracy of 98.6%. Thus, this work indicates the potential of a low-cost system for classifying healthy and diabetic individuals using an ANN algorithm.
Muhammad Fathi Yakan Zulkifli and Noorhamizah Mohamed Nasir, “Classification of Electromyography Signal of Diabetes using Artificial Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 13(11), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131149
@article{Zulkifli2022,
title = {Classification of Electromyography Signal of Diabetes using Artificial Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131149},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131149},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {11},
author = {Muhammad Fathi Yakan Zulkifli and Noorhamizah Mohamed Nasir}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.