ArticleScientific reports2024
Parkinson's disease detection based on features refinement through L1 regularized SVM and deep neural network.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- On the Suitability of Data Augmentation Techniques to Improve Parkinson's Disease Detection with Speech Recordings.Diagnostics (Basel, Switzerland) · 2026Article
- A novel class-attention transformer-driven feature fusion technique-based speech disorder classification.Frontiers in medicine · 2026Article
- Computational Approaches to Neurological Disorder Diagnosis: An In-Depth Review of Current Methods and Future ProspectsCurrent medical imaging · 2026Review
- Article
- Voice-Based Detection of Parkinson's Disease Using Machine and Deep Learning Approaches: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- A deep hybrid inception network model with entropy based attention for automated iron ore image characterization.Scientific reports · 2025Article
- Deep learning for property prediction of natural fiber polymer composites.Scientific reports · 2025Article
- A Unified Deep Learning Ensemble Framework for Voice-Based Parkinson's Disease Detection and Motor Severity Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Stacked Ensemble Learning for Classification of Parkinson's Disease Using Telemonitoring Vocal Features.Diagnostics (Basel, Switzerland) · 2025Article
- FCN-PD: An Advanced Deep Learning Framework for Parkinson's Disease Diagnosis Using MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- A quantum inspired machine learning approach for multimodal Parkinson's disease screening.Scientific reports · 2025Article
- Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson's Disease Using Vocal Characteristics: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.PloS one · 2025Article
- Non-invasive detection of Parkinson's disease based on speech analysis and interpretable machine learning.Frontiers in aging neuroscience · 2025Article
- A review of machine learning and deep learning for Parkinson's disease detection.Discover artificial intelligence · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In previous studies, replicated and multiple types of speech data have been used for Parkinson's disease (PD) detection. However, two main problems in these studies are lower PD detection accuracy and inappropriate validation methodologies leading to unreliable results. This study discusses the effects of inappropriate validation methodologies used in previous studies and highlights the use of appropriate alternative validation methods that would ensure generalization. To enhance PD detection accuracy, we propose a two-stage diagnostic system that refines the extracted set of features through [Formula: see text] regularized linear support vector machine and classifies the refined subset of features through a deep neural network. To rigorously evaluate the effectiveness of the proposed diagnostic system, experiments are performed on two different voice recording-based benchmark datasets. For both datasets, the proposed diagnostic system achieves 100% accuracy under leave-one-subject-out (LOSO) cross-validation (CV) and 97.5% accuracy under k-fold CV. The results show that the proposed system outperforms the existing methods regarding PD detection accuracy. The results suggest that the proposed diagnostic system is essential to improving non-invasive diagnostic decision support in PD.
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