ArticleBiomolecules2023
Prediction of Parkinson's Disease Using Machine Learning Methods.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 36 citations in OpenAlex.
- Artificial Intelligence Predicts GBA1 Mutated Status in Parkinson's Disease Patients.Movement disorders clinical practice · 2026Article
- Beyond the abdomen: an interpretable machine learning model for predicting postoperative ileus in non-abdominal surgery.Frontiers in physiology · 2026Article
- Dynamic context-aware multi-modal deep learning for longitudinal prediction of Parkinson's disease progression.Scientific reports · 2025Article
- Predictive modeling of motor symptom severity and stage classification in Parkinson's disease using machine learning methods with selected multiple serological biomarkers.BMC neurology · 2025Article
- An Explainable Approach to Parkinson's Diagnosis Using the Contrastive Explanation Method-CEM.Diagnostics (Basel, Switzerland) · 2025Article
- Comparison of 46 Cytokines in Peripheral Blood Between Patients with Papillary Thyroid Cancer and Healthy Individuals with AI-Driven Analysis to Distinguish Between the Two Groups.Diagnostics (Basel, Switzerland) · 2025Article
- A review of machine learning and deep learning for Parkinson's disease detection.Discover artificial intelligence · 2025Review
- From Night to Light: A Bibliometric Analysis of the Global Research Trajectory of Sleep Disorders in Parkinson's Disease.Journal of multidisciplinary healthcare · 2025Article
- Machine Learning for Early Detection of Cognitive Decline in Parkinson's Disease Using Multimodal Biomarker and Clinical Data.Biomedicines · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.