Robustness and Cross-Dataset Performance of Machine Learning Models in Parkinson’s Disease Diagnosis

• Ana Luísa B. Chagas UFG
• Giordana de Farias F. B. Bucci UFG
• Pedro L. S. Lobo UFG
• Rogerio L. Salvini UFG
• Fabrizzio A. Soares UFG
• Juliana P. Felix UFG / PUC-Goiás

DOI: https://doi.org/10.5753/erigo.2025.17137

Resumo

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.

Palavras-chave: Parkinson's disease, Machine Learning, Gait data, Cross-dataset validation, Diagnostic models

Referências

Balaji, E., Brindha, D., and Balakrishnan, R. (2020). Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease. Applied Soft Computing, 94:106494.

Beyrami, S. M. G. and Ghaderyan, P. (2020). A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. Measurement, 156:107579.

Braak, H. and Braak, E. (2000). Pathoanatomy of Parkinson’s disease. Journal of Neurology, 247:II3–II10.

Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220. Circulation Electronic Pages: [link] PMID:1085218; DOI: 10.1161/01.CIR.101.23.e215.

Hausdorff, J. (2000). Gait in neurodegenerative disease database. [link]. Last acessed in October 5th 2025.

Hausdorff, J. (2008). Gait in parkinson’s disease database. [link]. Last accessed in October 5th 2025.

Hausdorff, J. M., Lertratanakul, A., Cudkowicz, M. E., Peterson, A. L., Kaliton, D., and Goldberger, A. L. (2000). Dynamic Markers of Altered Gait Rhythm in Amyotrophic Lateral Sclerosis. Journal of Applied Physiology, 88(6):2045–2053.

Joshi, D., Khajuria, A., and Joshi, P. (2017). An automatic non-invasive method for parkinson’s disease classification. Computer Methods and Programs in Biomedicine, 145:135–145.

Mayeux, R. (2003). Epidemiology of neurodegeneration. Annual Review of Neuroscience, 26(1):81–104.

Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., Schrag, A.-E., and Lang, A. E. (2017). Parkinson disease. Nature Reviews Disease Primers, 3(1):17013.