Robustness and Cross-Dataset Performance of Machine Learning Models in Parkinson’s Disease Diagnosis - Chagas, Ana Luísa B.; Bucci, Giordana de Farias F. B.; Lobo, Pedro L. S.; Salvini, Rogerio L.; Soares, Fabrizzio A.; Felix, Juliana P. (Anais da Escola Regional de Informática de Goiás (ERI-GO); 2025: Anais da XIII Escola Regional de Informática de Goiás, 2025)
Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose diagnosis remains largely based on subjective clinical evaluation. In this study, we examine the generalization capacity of machine learning models for PD diagnosis from gait data. Descriptive features extracted from force signals were used to train multiple classifiers, evaluated through intra-dataset and cross-dataset experiments. Results showed consistent performance, particularly for ensemble methods such as ExtraTrees, demonstrating the robustness of the proposed approach. These findings highlight the importance of cross-dataset validation for assessing the real-world applicability of diagnostic models.