Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait

Fahmida Haque; Mamun Bin Ibne Reaz; Muhammad Enamul Hoque Chowdhury; Serkan Kiranyaz; Mohamed Abdelmoniem; Emadeddin Hussein; Mohammed Shaat; Sawal Hamid Md Ali; Ahmad Ashrif A Bakar; Geetika Srivastava; Mohammad Arif Sobhan Bhuiyan; Mohd Hadri Hafiz Mokhtar; Edi Kurniawan

arXiv · 2205.10581

Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait

Fahmida Haque·Mamun Bin Ibne Reaz·Muhammad Enamul Hoque Chowdhury·Serkan Kiranyaz·Mohamed Abdelmoniem·Emadeddin Hussein·Mohammed Shaat·Sawal Hamid Md Ali·Ahmad Ashrif A Bakar·Geetika Srivastava·Mohammad Arif Sobhan Bhuiyan·Mohd Hadri Hafiz Mokhtar·Edi Kurniawan

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Abstract

Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms of neuropathy affected by diabetic patients that involves alterations in biomechanical changes in human gait. In literature, for the last 50 years, researchers are trying to observe the biomechanical changes due to DSPN by studying muscle electromyography (EMG), and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we are proposing to use Machine learning techniques to identify DSPN patients by using EMG, and GRF data. We have collected a dataset consists of three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius medialis (GM) and 3-dimensional GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and a newly proposed feature extraction technique scheme from literature was applied to extract the best features from the signals. The extracted feature list was ranked using Relief feature ranking techniques, and highly correlated features were removed. We have trained different ML models to find out the best-performing model and optimized that model. We trained the optimized ML models for different combinations of muscles and GRF components features, and the performance matrix was evaluated. This study has found ensemble classifier model was performing in identifying DSPN Severity, and we optimized it before training. For EMG analysis, we have found the best accuracy of 92.89% using the Top 14 features for features from GL, VL and TA muscles combined. In the GRF analysis, the model showed 94.78% accuracy by using the Top 15 features for the feature combinations extracted from GRFx, GRFy and GRFz signals. The performance of ML-based DSPN severity classification models, improved significantly, indicating their reliability in DSPN severity classification, for biomechanical data.

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Fahmida Haque, Mamun Bin Ibne Reaz, Muhammad Enamul Hoque Chowdhury, Serkan Kiranyaz, Mohamed Abdelmoniem, Emadeddin Hussein, Mohammed Shaat, Sawal Hamid Md Ali, Ahmad Ashrif A Bakar, Geetika Srivastava, Mohammad Arif Sobhan Bhuiyan, Mohd Hadri Hafiz Mokhtar, Edi Kurniawan. 2022-05-21. Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait. https://arxiv.org/abs/2205.10581

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