Enhanced Binary Classification of Gait Disorders Using a Machine Learning Majority Voting Approach

This study introduces a machine learning-based methodology for classifying healthy individuals and those with gait disorders, employing a merged data set from 'GaitRec' and 'Gutenberg.' Key gait features were extracted from the normalized ground reaction force data that had 2435 subjects, including maximum peaks, load rate, lowest troughs, and area under the curve. Numerous machine learning models, such as Support Vector Machine, Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbor, Bagging, Adaboost, and Neural Network, were developed, and their hyperparameters were optimized using a grid search approach. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and F1 score. These assessment metrics are calculated through a repeated hold-out strategy, where 80% of the data is utilized for training the model, and the remaining 20% is reserved for testing purposes. Notably, the study highlights the superiority of an ensemble model that combines these algorithms through majority voting. This majority voting model, which was compared against individual models, achieved an accuracy of 96.63%, surpassing existing benchmarks in the field. This approach underscores the efficacy of ensemble techniques, particularly majority voting, in enhancing classification accuracy, thus contributing significantly to the field of biomedical signal processing by offering a scalable and cost-effective solution for accurate gait disorder identification.