{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:32:44Z","timestamp":1790620364886,"version":"4.1.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Despite substantial progress in biomarker research, Parkinson\u2019s disease (PD) still lacks widely validated, easily deployable diagnostic tests for reliable early-stage detection, particularly in resource-limited circumstances.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and externally validate a lightweight machine learning model for the first-diagnosis prediction of PD using baseline cerebrospinal fluid (CSF) biomarkers from the Parkinson\u2019s Progression Markers Initiative (PPMI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Baseline CSF data from 665 participants (PD = 415, controls = 190, SWEDD = 60) were used. Five machine learning classifiers\u2014L2-regularized logistic regression (L2-LR), random forest (RF), histogram-based gradient boosting (HistGB), support vector machine with RBF kernel (SVM-RBF), and multilayer perceptron (MLP)\u2014were trained and compared. Feature selection focused on five core CSF biomarkers (A\u03b242, \u03b1-synuclein, total tau, phosphorylated tau181 and hemoglobin). Model performance was evaluated using AUC, PR-AUC, and Brier scores, followed by isotonic calibration and independent validation using the University of Pennsylvania dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A lightweight, biomarker-based RF model effectively distinguishes first-diagnosis PD cases using limited baseline CSF indicators. Its offline Streamlit deployment offers a practical tool for resource-limited settings, bridging the gap between computational prediction and real-world neurological diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2025.1723169","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T06:26:29Z","timestamp":1765866389000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A lightweight cerebrospinal fluid biomarker-based model for first-diagnosis prediction of Parkinson\u2019s disease: model development, external validation, and local deployment"],"prefix":"10.3389","volume":"17","author":[{"given":"Xinchao","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunli","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing-Hua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"100034","DOI":"10.1016\/j.neumar.2024.100034","article-title":"The role of machine learning in discovering biomarkers and predicting treatment strategies for neurodegenerative diseases: A narrative review.","volume":"2","author":"Aborode","year":"2025","journal-title":"NeuroMarkers"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e46454","DOI":"10.7759\/cureus.46454","article-title":"Systematic review of the barriers to the implementation of artificial intelligence in healthcare.","volume":"15","author":"Ahmed","year":"2023","journal-title":"Cureus"},{"key":"B3","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1038\/s41531-024-00667-5","article-title":"Disease progression strikingly differs in research and real-world Parkinson\u2019s populations.","volume":"10","author":"Beaulieu-Jones","year":"2024","journal-title":"NPJ Parkinsons Dis."},{"key":"B4","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/S0140-6736(23)01419-8","article-title":"The epidemiology of Parkinson\u2019s disease.","volume":"403","author":"Ben-Shlomo","year":"2024","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.3390\/cells14161272","article-title":"Current status of \u03b1-Synuclein biomarkers and the need for \u03b1-Synuclein PET tracers.","volume":"14","author":"Berman","year":"2025","journal-title":"Cells"},{"key":"B6","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson\u2019s disease.","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"B7","doi-asserted-by":"publisher","first-page":"2534","DOI":"10.1021\/acs.analchem.3c04849","article-title":"Intelligent identification of cerebrospinal fluid for the diagnosis of Parkinson\u2019s disease.","volume":"96","author":"Chen","year":"2024","journal-title":"Anal. 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