{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:40:03Z","timestamp":1778085603373,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PhD Program in Health Data Science of the Faculty of Medicine of the University of Porto, Portugal, heads.med.up.pt"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Parkinson\u2019s Disease significantly impacts health-related quality of life, with the Parkinson\u2019s Disease Questionnaire-39 extensively used for its assessment. However, predicting such outcomes remains a challenge due to the subjective nature and variability in patient experiences. This study develops a machine learning model using accessible clinical data to enable predictions of life-quality outcomes in Parkinson\u2019s Disease and utilizes explainable machine learning techniques to identify key influencing factors, offering actionable insights for clinicians. Methods: Data from the Parkinson\u2019s Real-world Impact Assessment study (PRISM), involving 861 patients across six European countries, were analyzed. After excluding incomplete data, 627 complete observations were used for the analysis. An ensemble machine learning model was developed with a 90% training and 10% validation split. Results: The model demonstrated a Mean Absolute Error of 4.82, a Root Mean Squared Error of 8.09, and an R2 of 0.75 in the training set, indicating a strong model fit. In the validation set, the model achieved a Mean Absolute Error of 11.22, a Root Mean Squared Error of 13.99, and an R2 of 0.36, showcasing moderate variation. Key predictors such as age at diagnosis, patient\u2019s country, dementia, and patient\u2019s age were identified, providing insights into the model\u2019s decision-making process. Conclusions: This study presents a robust model capable of predicting the impact of Parkinson\u2019s Disease on patients\u2019 quality of life using common clinical variables. These results demonstrate the potential of machine learning to enhance clinical decision-making and patient care, suggesting directions for future research to improve model generalizability and applicability.<\/jats:p>","DOI":"10.3390\/jcm13175081","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T09:26:51Z","timestamp":1724750811000},"page":"5081","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predicting Quality of Life in Parkinson\u2019s Disease: A Machine Learning Approach Employing Common Clinical Variables"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3887-7620","authenticated-orcid":false,"given":"Daniel","family":"Magano","sequence":"first","affiliation":[{"name":"Ph.D. Program in Health Data Science, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"},{"name":"Medical Department, BIAL-Portela & C\u00aa., S.A., 4745-457 S\u00e3o Mamede do Coronado, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0998-6000","authenticated-orcid":false,"given":"Tiago","family":"Taveira-Gomes","sequence":"additional","affiliation":[{"name":"Department of Community Medicine, Information and Decision in Health, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"},{"name":"Faculty of Health Sciences, University Fernando Pessoa, 4200-150 Porto, Portugal"},{"name":"SIGIL Scientific Enterprises, 4076 Dubai, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5791-7149","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Massano","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurosciences and Mental Health, Faculty of Medicine University of Porto, 4200-319 Porto, Portugal"},{"name":"Department of Neurology, Centro Hospitalar Universit\u00e1rio de S\u00e3o Jo\u00e3o, 4200-319 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9103-5852","authenticated-orcid":false,"given":"Ant\u00f3nio S.","family":"Barros","sequence":"additional","affiliation":[{"name":"Department of Surgery and Physiology, Cardiovascular R&D Centre-UnIC@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/S1474-4422(17)30299-5","article-title":"Global, Regional, and National Burden of Neurological Disorders during 1990\u20132015: A Systematic Analysis for the Global Burden of Disease Study 2015","volume":"16","author":"Feigin","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1136\/jnnp.2007.131045","article-title":"Parkinson\u2019s Disease: Clinical Features and Diagnosis","volume":"79","author":"Jankovic","year":"2008","journal-title":"J. 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