Publicação
Parkinson’s Disease and Atypical Parkinsonism in the context of rehabilitation: a machine learning approach
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Vanneschi, Leonardo | |
| dc.contributor.author | Pires, Susana Cristina Norberto | |
| dc.date.accessioned | 2025-11-11T12:05:37Z | |
| dc.date.available | 2025-11-11T12:05:37Z | |
| dc.date.issued | 2025-10-28 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science | pt_PT |
| dc.description.abstract | The increasing use of artificial intelligence in healthcare offers significant opportunities to improve patient outcomes and optimize clinical workflows. From the diversity of applications, machine learning continues to demonstrate its potential of use as an auxiliary diagnostic tool. It is used in the context of neurodegenerative diseases research, for tasks such as speech and handwriting analysis, gait impairment studies using sensor data, and to discover new potential pharmacotherapies. Parkinson’s disease and atypical parkinsonism are two classes of neurodegenerative pathologies that present significant challenges in differential diagnosis and require distinct management strategies. Distinguishing them early allows for a better prognosis by targeting the specific characteristics of each disease. Despite the advancements in machine learning research applied to Parkinson’s, there is a gap in studies that specifically address the binary classification between the two classes, which is the focus of this thesis. This research can serve as a starting point to the development of a complementary tool to assist practitioners with early differential diagnosis, using data from standardised clinical assessments. A comprehensive experimental design was implemented, using six machine learning classifiers. One of the tested implementations accounts for strategies to address class imbalance and small sample size. The objectives were to analyse and identify the bestperforming model, and to determine which features impact classification the most. The results, when accounted for F1-score macro, indicate that extreme gradient boosting and random forest achieved the highest scores. Statistical testing revealed no significant difference in performance between these two models. When balanced accuracy was considered, logistic regression, support vector classifier and extreme gradient boosting achieved the highest scores and also presented no statistically significant differences in performance. Although the performance metrics were not as good as expected, the best models achieved scores ranging from 0.55 to 0.62 for both F1-score macro and balanced accuracy. This research is a foundation for the classification task of recognizing each disease. Additionally, model interpretability was explored using Shapley additive explanations. The feature “mds_updrs_pIII” was found to be the most influential in distinguishing between the two conditions, followed by the “Age”. These insights can inform future scientific research. Despite limitations in data quality and sample size, this work provides a stable methodology for future studies with richer datasets, more features, and alternative modelling approaches. Ultimately, this research emphasisesthe value of interdisciplinary collaboration and how both positive and negative results contribute to scientific progress. | pt_PT |
| dc.identifier.tid | 204072441 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190475 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Artificial Intelligence | pt_PT |
| dc.subject | Parkinson’s Disease | pt_PT |
| dc.subject | Atypical Parkinsonian Disorders | pt_PT |
| dc.subject | Atypical Parkinsonism | pt_PT |
| dc.subject | Healthcare | pt_PT |
| dc.subject | Rehabilitation | pt_PT |
| dc.subject | SDG 3 - Good health and well-being | pt_PT |
| dc.title | Parkinson’s Disease and Atypical Parkinsonism in the context of rehabilitation: a machine learning approach | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Data Science | pt_PT |