A novel dynamic weighted prediction framework with stability-enhanced dynamic thresholding feature selection for neurodegenerative disease detection using gait features

Gait dynamics are widely used to diagnose neurodegenerative illnesses like ALS, Parkinson’s, and Huntington’s. Cognitive bias might hinder clinical assessments and early detection. Machine learning may help detect aberrant gait patterns early. A novel ensemble classifier, the Dynamic Weighted Prediction Framework (DWPF), and an innovative feature selection methodology, Stability-Enhanced Dynamic Thresholding (SEDT), have been proposed for neurodegenerative disease detection. DWPF combines predictions from various heterogeneous base classifiers using dynamic weighted averaging with weights based on performance. By adapting to classifier efficacies, this method improves ensemble accuracy. The DWPF’s dynamic weight allocation based on real-time classifier performance makes the model robust across datasets and classification problems. This strategy balances each base classifier's reliability to improve accuracy and model resilience to overfitting and underfitting. SEDT uses stability enhancement and dataset-specific feature relevance thresholds to identify significant neurodegenerative disease features before classification. Unlike fixed threshold approaches, SEDT may adjust to different data subsets' feature relevance, preserving only the most important features for model training. The DWPF ensemble and SEDT feature selection approach are merged to detect neurodegenerative diseases in gait data. Extensive binary classification testing has been conducted to discriminate controls from ALS, Parkinson's, and Huntington’s patients. The proposed SEDT + DWPF model was validated using Holdout Validation, K-Fold Cross-Validation, and Monte Carlo (Shuffle-Split) Cross-Validation. Monte Carlo Cross-Validation had the best ALS detection accuracy (93.50%), sensitivity (94.63%), and specificity (92.80%). K-Fold Cross-Validation detected Huntington's disease with the best accuracy (81.09%), sensitivity (74.19%), and specificity (89.28%). Monte Carlo Cross-Validation performed best for Parkinson's disease identification, with 84.58% accuracy, 78.95% sensitivity, and 89.71% specificity. DWPF-SEDT integration improves neurodegenerative disease diagnosis using adaptive feature selection and robust ensemble classification. This method has great potential for early and accurate neurodegenerative disease diagnosis.