  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.
