Non-motor symptoms as critical predictors of quality of life in Parkinson’s disease: a machine learning approach
Our findings demonstrate the critical role of non-motor symptoms in predicting HRQoL in patients with PD. While ML models effectively predict overall HRQoL and cognitive aspects, achieving comparable performance on other dimensions may require additional variables to reduce error. These insights emphasize comprehensive treatment strategies addressing both motor and non-motor symptoms.
Authors
- António S. Barros (ORCID: https://orcid.org/0000-0002-9103-5852)
- João Massano (ORCID: https://orcid.org/0000-0002-5791-7149)
- Laila Alsuwaidi (ORCID: https://orcid.org/0000-0002-6723-0770)
- Tiago Taveira‐Gomes (ORCID: https://orcid.org/0000-0002-0998-6000)
- Daniel Magano (ORCID: https://orcid.org/0000-0002-3887-7620)
Institutions
- Bial (Portugal) (PT)
- Fernando Pessoa University (PT)
- Universidade do Porto (PT)
- Hospital de São João (PT)
- Decision Sciences (United States) (US)
- University of Modern Sciences (AE)
Publication Details
- Journal
- Health and Quality of Life Outcomes
- Published
- 2025-12-06
- DOI
- https://doi.org/10.1186/s12955-025-02451-2
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00