ArticleSensors (Basel, Switzerland)2018
Classifying Parkinson's Disease Based on Acoustic Measures Using Artificial Neural Networks.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2022Pooled it
- Alpha-synuclein overexpression reduces neural activity within a basal ganglia vocal nucleus in a zebra finch model.PloS one · 2026Article
- Predicting Cognitive Decline in Parkinson's Disease Using Artificial Neural Networks: An Explainable AI Approach.Brain sciences · 2025Article
- Advanced optimization strategies for combining acoustic features and speech recognition error rates in multi-stage classification of Parkinson's disease severity.Biomedical engineering letters · 2025Article
- Normative aging results in degradation of gene networks in a zebra finch basal ganglia nucleus dedicated to vocal behavior.Neurobiology of aging · 2025Article
- Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson's Disease Using Vocal Characteristics: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Parkinson's disease detection using spectrogram-based multi-model feature fusion networks.Frontiers in neurology · 2025Article
- Gait classification for early detection and severity rating of Parkinson's disease based on hybrid signal processing and machine learning methods.Cognitive neurodynamics · 2024Article
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- Analysis of Parkinson's Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods.Diagnostics (Basel, Switzerland) · 2022Article
- A generic optimization and learning framework for Parkinson disease via speech and handwritten records.Journal of ambient intelligence and humanized computing · 2022Article
- Classification of Dysphonic Voices in Parkinson's Disease with Semi-Supervised Competitive Learning Algorithm.Biosensors · 2022Article
- Machine Learning Smart System for Parkinson Disease Classification Using the Voice as a Biomarker.Healthcare informatics research · 2022Article
- A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders.Biology · 2022Review
- Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem.Medicina (Kaunas, Lithuania) · 2021Article
- Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network.Materials (Basel, Switzerland) · 2021Article
- Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence.Journal of personalized medicine · 2021Review
- Advanced comparative analysis of machine learning algorithms for early Parkinson's disease detection using vocal biomarkers.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson's disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson's correlation coefficient, Kendall's correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall's correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.