ArticleScientific reports2025
Machine learning for early detection and severity classification in people with Parkinson's disease.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Wearable device for tremor-like motion acquisition: a low-cost device using 3D printing for research and education.HardwareX · 2026Article
- CNN-GRU-KAN: A Novel Multi-Branch Framework for Parkinson's Disease Detection Based on Gait Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.Scientific reports · 2026Article
- Current Trends in AI Gait Analysis for the Detection and Assessment of Parkinson's Disease Severity: Systematic Review and Meta-Analysis of Performance Using Logit Transformation.Healthcare (Basel, Switzerland) · 2026Review
- Machine Learning Aided Kinematic Profiling of Reaching Movements Separates Spinocerebellar Ataxia type 12 and Essential Tremor.Cerebellum (London, England) · 2026Article
- Machine learning classification of early-stage Parkinson's disease using sit-to-walk biomechanical features.Scientific reports · 2026Article
- Volumetric signatures of basal ganglia-thalamo-cortical and cerebello-thalamo-cortical networks in Parkinson's disease and its motor subtypes.Frontiers in aging neuroscience · 2026Article
- Enhanced EfficientNet-Extended Multimodal Parkinson's disease classification with Hybrid Particle Swarm and Grey Wolf Optimizer.Scientific reports · 2025Article
- Multi-Domain CoP Feature Analysis of Functional Mobility for Parkinson's Disease Detection Using Wearable Pressure Insoles.Sensors (Basel, Switzerland) · 2025Article
- Characterizing Stair Ambulation Kinetics and the Effects of Dual Tasking in Parkinson's Disease.Journal of clinical medicine · 2025Article
- The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions.Toxics · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Early detection of Parkinson's disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom severity using gait analysis. This study evaluated the accuracy of machine learning models in classifying early and moderate-stages of PD based on spatiotemporal gait features at different walking speeds. A total of 178 participants were recruited, including 103 individuals with PD (61 early-stage, 42 moderate-stage) and 75 healthy controls. Participants performed a walking test on a 24-m walkway at three speeds: preferred walking speed (PWS), 20% faster (HWS), and 20% slower (LWS). Key features-walking speed at PWS, stride length at HWS, and the coefficient of variation (CV) of the stride length at LWS-achieved a classification accuracy of 78.1% using the random forest algorithm. For early PD detection, the stride length at HWS and CV of the stride length at LWS provided an accuracy of 67.3% with Naïve Bayes. Walking at PWS was the most critical feature for distinguishing early from moderate PD, with an accuracy of 69.8%. These findings suggest that assessing gait over consecutive steps under different speed conditions may improve the early detection and severity assessment of individuals with PD.
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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.