ArticleScientific reports2024
Detection of Parkinson disease using multiclass machine learning approach.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A task-aligned multimodal machine learning framework for studying working memory dysfunction in Parkinson's disease.Cognitive neurodynamics · 2026Article
- A Comparison of Machine Learning Models for Classification of Parkinson's Disease During a Working Memory and Sustained Attention Task.Brain sciences · 2026Article
- Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.Scientific reports · 2026Article
- Enhancing Parkinson's Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection.Journal of molecular neuroscience : MN · 2026Article
- Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases.PeerJ. Computer science · 2026Review
- A quantum-classical dual-track deep learning network for explainable Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- Voice-Based Detection of Parkinson's Disease Using Machine and Deep Learning Approaches: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Multi-Modal Decentralized Hybrid Learning for Early Parkinson's Detection Using Voice Biomarkers and Contrastive Speech Embeddings.Sensors (Basel, Switzerland) · 2025Article
- A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson's Disease and Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis.Scientific reports · 2025Article
- Parkinson's Disease Diagnosis and Severity Assessment from Gait Signals via Bayesian-Optimized Deep Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Independent component analysis of oddball EEG recordings to detect Parkinson's disease.Scientific reports · 2025Article
- Article
- A Hybrid Convolutional-Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- A quantum inspired machine learning approach for multimodal Parkinson's disease screening.Scientific reports · 2025Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- Particle swarm optimization framework for Parkinson's disease prediction.PeerJ. Computer science · 2025Article
- Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80-20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson's Disease.
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