ArticleBiomedical engineering letters2018
Performance of machine learning methods in diagnosing Parkinson's disease based on dysphonia measures.
Article in Biomedical engineering letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 126 citations in OpenAlex.
- Advancing the development of deep learning and machine learning models for oral drugs through diverse descriptor classes: a focus on pharmacokinetic parameters (Vdss and PPB).Molecular diversity · 2026Article
- Machine Learning-Based Detection of Parkinson's Disease From Arabic Speech: A Cross-Linguistic Validation Study.Journal of central nervous system disease · 2026Article
- FedOcw: optimized federated learning for cross-lingual speech-based Parkinson's disease detection.NPJ digital medicine · 2025Article
- Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2025Article
- Applications of machine learning for computer-aided diagnosis of Parkinson's disease: progress and benchmark case study.Artificial intelligence review · 2025Article
- Simultaneous Utilization of Mood Disorder Questionnaire and Bipolar Spectrum Diagnostic Scale for Machine Learning-Based Classification of Patients With Bipolar Disorders and Depressive Disorders.Psychiatry investigation · 2024Article
- Serum Metabolites as Potential Markers and Predictors of Depression-like Behavior and Effective Fluoxetine Treatment in Chronically Socially Isolated Rats.Metabolites · 2024Article
- Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns.Bioengineering (Basel, Switzerland) · 2024Article
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- A machine learning method to process voice samples for identification of Parkinson's disease.Scientific reports · 2023Article
- Towards a Corpus (and Language)-Independent Screening of Parkinson's Disease from Voice and Speech through Domain Adaptation.Bioengineering (Basel, Switzerland) · 2023Article
- Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding.Advanced drug delivery reviews · 2023Review
- Metabolic Fingerprints of Effective Fluoxetine Treatment in the Prefrontal Cortex of Chronically Socially Isolated Rats: Marker Candidates and Predictive Metabolites.International journal of molecular sciences · 2023Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- The Role of Neural Network for the Detection of Parkinson's Disease: A Scoping Review.Healthcare (Basel, Switzerland) · 2021Article
- A new approach: information gain algorithm-based k-nearest neighbors hybrid diagnostic system for Parkinson's disease.Physical and engineering sciences in medicine · 2021Article
- EEG microstate features for schizophrenia classification.PloS one · 2021Article
- An Intelligent Mobile-Enabled System for Diagnosing Parkinson Disease: Development and Validation of a Speech Impairment Detection System.JMIR medical informatics · 2020Article
- An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease.Journal of neuroscience methods · 2020Article
- Multivariate association between brain function and eating disorders using sparse canonical correlation analysis.PloS one · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were considered and trained with a set of twenty-two voice disorder measurements to classify healthy and PD patients. These machine learning methods included linear discriminant analysis (LDA),
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