Optimizing Diabetic Neuropathy Severity Classification Using Electromyography Signals Through Synthetic Oversampling Techniques

I Ketut Adi Purnawan; Adhi Dharma Wibawa; Arik Kurniawati; Mauridhi Hery Purnomo

doi:10.23887/janapati.v13i3.85675

Authors

I Ketut Adi Purnawan Departement of Electrical Engineering, Institut Teknologi Sepuluh Nopember
Adhi Dharma Wibawa Department of Medical Technology and Engineering, Institut Teknologi Sepuluh Nopember
Arik Kurniawati Department of Informatics, Universitas Trunojoyo Madura
Mauridhi Hery Purnomo Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.23887/janapati.v13i3.85675

Keywords:

electromyography, diabetes neuropathy, ROS, SMOTE, XGBoost

Abstract

Electromyography signals are electrical signals generated by muscle activity and are very useful for analyzing the health conditions of muscles and nerves. Data imbalance is a prevalent issue in EMG signal data, especially when addressing patients with varied health conditions and restricted data availability. A major difficulty for machine learning models is class imbalance in datasets, which frequently leads to biased predictions favoring the dominant class and neglecting the minority classes. The data augmentation method employs the Synthetic Minority Over Sampling Technique (SMOTE) and Random Over Sampling (ROS) to address data imbalances and enhance the performance of classification models for underrepresented classes. This study employs an oversampling technique to enhance the efficacy of the XG Boost model. SMOTE exhibits better efficacy relative to competing methods; the application of appropriate oversampling techniques allows models to integrate patterns from both majority and often neglected minority data.

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