ArticlePloS one2017
Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Smartphone-Based Multimodal Digital Biomarker Integration for Parkinson's Disease Screening and Diagnostic Support.Neurology and therapy · 2026Article
- A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study.JMIR medical informatics · 2026Article
- Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease.Movement disorders clinical practice · 2026Review
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Through the Speech and Vocal Signals Hidden Secrets: An Explainable Methodology for Neurological Diseases Early Detection.Journal of healthcare informatics research · 2025Article
- Stacked Ensemble Learning for Classification of Parkinson's Disease Using Telemonitoring Vocal Features.Diagnostics (Basel, Switzerland) · 2025Article
- A unified approach for Parkinson's disease recognition: imbalance mitigation and grid search optimized boosting with LightGBM.Medical & biological engineering & computing · 2024Article
- Article
- Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers.Journal of Parkinson's disease · 2024Article
- Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.Frontiers in immunology · 2023Article
- An explainable COVID-19 detection system based on human sounds.Smart health (Amsterdam, Netherlands) · 2022Article
- COVID-19 and Integrated Multidisciplinary Care Model in Parkinson's Disease: Literature Review & Future Perspectives.Behavioral sciences (Basel, Switzerland) · 2022Review
- QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds.Diagnostics (Basel, Switzerland) · 2022Article
- Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance.Concurrency and computation : practice & experience · 2022Article
- Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson's Disease.Journal of healthcare engineering · 2022Article
- COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds.The European physical journal. Special topics · 2022Article
- An algorithm for Parkinson's disease speech classification based on isolated words analysis.Health information science and systems · 2021Article
- Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson's Disease Detection.Diagnostics (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
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recently proposed Parkinson's Disease (PD) telediagnosis systems based on detecting dysphonia achieve very high classification rates in discriminating healthy subjects from PD patients. However, in these studies the data used to construct the classification model contain the speech recordings of both early and late PD patients with different severities of speech impairments resulting in unrealistic results. In a more realistic scenario, an early telediagnosis system is expected to be used in suspicious cases by healthy subjects or early PD patients with mild speech impairment. In this paper, considering the critical importance of early diagnosis in the treatment of the disease, we evaluate the ability of vocal features in early telediagnosis of Parkinson's Disease (PD) using machine learning techniques with a two-step approach. In the first step, using only patient data, we aim to determine the patient group with relatively greater severity of speech impairments using Unified Parkinson's Disease Rating Scale (UPDRS) score as an index of disease progression. For this purpose, we use three supervised and two unsupervised learning techniques. In the second step, we exclude the samples of this group of patients from the dataset, create a new dataset consisting of the samples of PD patients having less severity of speech impairments and healthy subjects, and use three classifiers with various settings to address this binary classification problem. In this classification problem, the highest accuracy of 96.4% and Matthew's Correlation Coefficient of 0.77 is obtained using support vector machines with third-degree polynomial kernel showing that vocal features can be used to build a decision support system for early telediagnosis of PD.
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