ArticleJournal of healthcare engineering2021
Assessment of Acoustic Features and Machine Learning for Parkinson's Detection.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- AI-Driven Advances in Parkinson's Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes.Brain sciences · 2025Review
- Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers.Journal of Parkinson's disease · 2024Article
- Radiomics for Parkinson's disease classification using advanced texture-based biomarkers.MethodsX · 2023Article
- Retinal Changes in Parkinson's Disease: A Non-invasive Biomarker for Early Diagnosis.Cellular and molecular neurobiology · 2023Review
- A review of emergent intelligent systems for the detection of Parkinson's disease.Biomedical engineering letters · 2023Review
- Parkinson's Disease Detection Using Filter Feature Selection and a Genetic Algorithm with Ensemble Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Machine Learning Framework for Multistage Parkinson's Disease Classification Using Acoustic Features of Sustained Korean Vowels.Bioengineering (Basel, Switzerland) · 2023Article
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
- Audio Recording Patient-Nurse Verbal Communications in Home Health Care Settings: Pilot Feasibility and Usability Study.JMIR human factors · 2022Article
- Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review.Metabolites · 2022Review
- Metaheuristics with Deep Learning-Enabled Parkinson's Disease Diagnosis and Classification Model.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents a machine learning approach for Parkinson's disease detection. Potential multiple acoustic signal features of Parkinson's and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson's magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.
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Registered trials
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