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

Biomechanics (9) Machine Learning (1717) Digital Biomarkers and Digital Phenotyping (226) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1562) Cognitive and Neurorehabilitation (300) Computational modeling of biological processes (12)

Published on in Vol 5, No 1 (2020): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13611, first published .
Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques

Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques

Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques

Authors of this article:

Max Wang1 Author Orcid Image ;   Wenbo Ge1 Author Orcid Image ;   Deborah Apthorp1, 2 Author Orcid Image ;   Hanna Suominen1, 3, 4 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Aborageh M, Krawitz P, Fröhlich H. Genetics in parkinson’s disease: From better disease understanding to machine learning based precision medicine. Frontiers in Molecular Medicine 2022;2 View
  2. Dixit S, Bohre K, Singh Y, Himeur Y, Mansoor W, Atalla S, Srinivasan K. A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis. Electronics 2023;12(4):783 View
  3. Saravanan S, Ramkumar K, Adalarasu K, Sivanandam V, Kumar S, Stalin S, Amirtharajan R. A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease. Archives of Computational Methods in Engineering 2022;29(6):3639 View
  4. Rastpour A, McGregor C. Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach. JMIR Mental Health 2022;9(8):e38428 View
  5. F. Alenezi D, Shi H, Li J. A Ranking-based Weakly Supervised Learning model for telemonitoring of Parkinson’s disease. IISE Transactions on Healthcare Systems Engineering 2022;12(4):322 View
  6. Ge W, Lueck C, Suominen H, Apthorp D. Has machine learning over-promised in healthcare?. Artificial Intelligence in Medicine 2023;139:102524 View
  7. Keserwani P, Das S, Sarkar N. A comparative study: prediction of parkinson’s disease using machine learning, deep learning and nature inspired algorithm. Multimedia Tools and Applications 2024;83(27):69393 View
  8. Oyebola K, Ligali F, Owoloye A, Erinwusi B, Alo Y, Musa A, Aina O, Salako B. Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals. JMIRx Med 2024;5:e56993 View
  9. 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

Conference Proceedings

  1. Magare A, Patel M. 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV). Biomarkers Identification for Parkinson’s Disease using Machine Learning View
  2. Reddy C, Kanchana M. 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). Artificial Intelligence towards Parkinson’s disease Diagnosis: A systematic Review of Contemporary Literature View
  3. Shah R, Dave B, Parekh N, Srivastava K. 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT). Parkinson's Disease Detection - An Interpretable Approach to Temporal Audio Classification View
  4. Joses S, Saikhu A. 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). Enhancing XGBoost and CatBoost Methods for Diagnosing Parkinson's Disease Through the Integration of SMOTE and Feature Selection Techniques View