This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (106) Neurotech Innovations, Diagnostic Tools and Techniques (49) Cognitive and Neurorehabilitation (326) Clinical Information and Decision Making (1806) Digital Health Reviews (1650)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50117, first published .
Machine Learning Models for Parkinson Disease: Systematic Review

Machine Learning Models for Parkinson Disease: Systematic Review

Machine Learning Models for Parkinson Disease: Systematic Review

Authors of this article:

Thasina Tabashum1 Author Orcid Image ;   Robert Cooper Snyder1 Author Orcid Image ;   Megan K O'Brien2, 3 Author Orcid Image ;   Mark V Albert1, 4 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Nehmeh B, Rebehmed J, Nehmeh R, Taleb R, Akoury E. Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases. Drug Discovery Today 2024;29(12):104216 View
  2. Calderone A, Latella D, Bonanno M, Quartarone A, Mojdehdehbaher S, Celesti A, Calabrò R. Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders. Biomedicines 2024;12(10):2415 View
  3. S.P. R, M. S, R. J. Streamlit-based Web Application for Parkinson's Detection using Machine Learning. Journal of Artificial Intelligence and Capsule Networks 2025;6(4):466 View
  4. Rissardo J, Caprara A. A Narrative Review on Biochemical Markers and Emerging Treatments in Prodromal Synucleinopathies. Clinics and Practice 2025;15(3):65 View
  5. Zhao Z, Zou P, Fang Y, Si T, Li Y, Yi B, Zhang T. Machine learning approaches for assessing medication transfer to human breast milk. Journal of Pharmacokinetics and Pharmacodynamics 2025;52(3) View
  6. Madusanka N, Malekroodi H, Herath H, Hewage C, Yi M, Lee B. Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection. Journal of Imaging 2025;11(7):220 View
  7. Starcke J, Spadafora J, Spadafora J, Spadafora P, Toma M. The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson’s Disease Detection. Bioengineering 2025;12(8):845 View
  8. Brizzi A, Pinto Neto O, Pedreiro R, Moreira L. High-Accuracy Classification of Parkinson’s Disease Using Ensemble Machine Learning and Stabilometric Biomarkers. Neurology International 2025;17(9):133 View
  9. Egbo B, Nigmetolla Z, Khan N, Jamwal P. Explainable machine learning for early detection of Parkinson’s disease in aging populations using vocal biomarkers. Frontiers in Aging Neuroscience 2025;17 View

Conference Proceedings

  1. Sharif K, Uddin M, Abubakkar M. 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). NeuroSignal Precision: A Hierarchical Approach for Enhanced Insights in Parkinson's Disease Classification View
  2. Tabashum T, Wang S, Krzak J, Kruger K, Graf A, Chafetz R, Davids J, Bagley A, Woloff J, Sienko S, Bauer J, Albert M. 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Language Models for Fall Risk Assessment in Children With Cerebral Palsy Using Electronic Medical Records View
  3. Solaiman F, Solaiman M, Neary K. 2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC). Voice-Driven Diagnostics: Leveraging Machine Learning for Early Detection of Parkinson's Disease from Acoustic Biomarkers View
  4. Maheshwari A, Banerjee I, Jayaraman R. 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). Parkison Disease Prediction using Machine Learning In Healthcare View