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An Analysis of Vocal Features for Parkinson’s Disease Classification Using Evolutionary Algorithms

https://doi.org/10.3390/diagnostics12081980
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mds.26424openalex-snapshot
MDS clinical diagnostic criteria for Parkinson's disease
inferredcites →https://doi.org/10.1007/978-3-030-70111-6openalex-snapshot
Enhanced Telemedicine and e-Health
inferredcites →https://doi.org/10.1007/s00702-018-1910-4openalex-snapshot
Diagnostic biomarkers for Parkinson’s disease at a glance: where are we?
inferredcites →https://doi.org/10.1007/s10916-016-0477-6openalex-snapshot
A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM
inferredcites →https://doi.org/10.1016/j.advengsoft.2013.12.007openalex-snapshot
Grey Wolf Optimizer
inferredcites →https://doi.org/10.1016/j.asoc.2018.10.022openalex-snapshot
A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform
inferredcites →https://doi.org/10.1016/j.bbe.2019.05.006openalex-snapshot
Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels
inferredcites →https://doi.org/10.1016/j.bspc.2021.102415openalex-snapshot
Analysis of voice as an assisting tool for detection of Parkinson's disease and its subsequent clinical interpretation
inferredcites →https://doi.org/10.1016/j.elerap.2018.08.002openalex-snapshot
Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning
inferredcites →https://doi.org/10.1016/j.neucom.2015.06.083openalex-snapshot
Binary grey wolf optimization approaches for feature selection
inferredcites →https://doi.org/10.1016/j.patrec.2014.02.013openalex-snapshot
t-Test feature selection approach based on term frequency for text categorization
inferredcites →https://doi.org/10.1038/s42256-019-0138-9openalex-snapshot
From local explanations to global understanding with explainable AI for trees
inferredcites →https://doi.org/10.1044/1092-4388(2008/043)openalex-snapshot
Articulatory Movements During Vowels in Speakers With Dysarthria and Healthy Controls
inferredcites →https://doi.org/10.1109/access.2019.2936564openalex-snapshot
Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets
inferredcites →https://doi.org/10.1109/access.2020.3047942openalex-snapshot
A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic
inferredcites →https://doi.org/10.1109/atc50776.2020.9255445openalex-snapshot
Predicting heart failure using deep neural network
inferredcites →https://doi.org/10.1109/ebbt.2019.8741725openalex-snapshot
A Hybrid Approach to Parkinson Disease Classification Using Speech Signal: The Combination of SMOTE and Random Forests
inferredcites →https://doi.org/10.1109/iciip47207.2019.8985722openalex-snapshot
Levy inspired Enhanced Grey Wolf Optimizer
inferredcites →https://doi.org/10.1109/lsp.2005.860538openalex-snapshot
Combining evidence from residual phase and MFCC features for speaker recognition
inferredcites →https://doi.org/10.1109/tbme.2006.871883openalex-snapshot
Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters
inferredcites →https://doi.org/10.1109/tbme.2012.2183367openalex-snapshot
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease
inferredcites →https://doi.org/10.1136/jnnp.2007.131045openalex-snapshot
Parkinson's disease: clinical features and diagnosis
inferredcites →https://doi.org/10.1136/jnnp.51.6.745openalex-snapshot
The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease.
inferredcites →https://doi.org/10.11591/ijeecs.v21.i3.pp1530-1539openalex-snapshot
Predicting heart failure using a wrapper-based feature selection
inferredcites →https://doi.org/10.3389/fneur.2020.00180openalex-snapshot
Progressive Supranuclear Palsy—Parkinsonism Predominant (PSP-P)—A Clinical Challenge at the Boundaries of PSP and Parkinson's Disease (PD)
inferredcites →https://doi.org/10.3389/fneur.2022.831428openalex-snapshot
Voice in Parkinson's Disease: A Machine Learning Study
inferredcites →https://doi.org/10.5121/mlaij.2016.3203openalex-snapshot
Machine Learning Based Approaches for Prediction of Parkinson's Disease
inferredcites →https://doi.org/10.5603/pjnns.a2022.0014openalex-snapshot
Platelet-to-lymphocyte ratio and neutrophil-tolymphocyte ratio may reflect differences in PD and MSA-P neuroinflammation patterns

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Metaheuristic Feature Selection in Voice-Based Parkinson’s Disease Classification
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Overview of Voice-Related Parameters in Parkinson’s Disease: Aspects of Biomarkers
inferred← citeshttps://doi.org/10.1007/978-3-032-38806-3_11openalex-snapshot
Hybrid Model Based on Recurrent Neural Networks and Convolutional Neural Networks for the Early Diagnosis of Parkinson’s Disease Based on Voice Analysis
inferred← citeshttps://doi.org/10.1007/978-981-97-1463-6_39openalex-snapshot
Voice Features Examination for Parkinson’s Disease Detection Utilizing Machine Learning Methods
inferred← citeshttps://doi.org/10.1007/s42452-026-08274-0openalex-snapshot
Voice-driven Parkinson’s disease prediction using a chaotic Grey Wolf–Dragonfly algorithm in high-dimensional datasets
inferred← citeshttps://doi.org/10.1007/s42979-025-04694-8openalex-snapshot
A New Evolutionary Fuzzy K-Nearest Neighbor with Application to Parkinson’s Diagnosis
inferred← citeshttps://doi.org/10.1016/j.arr.2024.102285openalex-snapshot
Parkinson’s disease diagnosis using deep learning: A bibliometric analysis and literature review
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107142openalex-snapshot
Bayesian optimization enhanced FKNN model for Parkinson’s diagnosis
inferred← citeshttps://doi.org/10.1016/j.isci.2026.117185openalex-snapshot
Impact of Amazonian dance on speech performance in people with Parkinson’s disease
inferred← citeshttps://doi.org/10.1016/j.jvoice.2024.04.020openalex-snapshot
Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112950openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/21642583.2025.2498909openalex-snapshot
Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE
inferred← citeshttps://doi.org/10.1088/2632-2153/adadc3openalex-snapshot
Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning
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inferred← citeshttps://doi.org/10.1109/atc63255.2024.10908129openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cine63708.2024.10881138openalex-snapshot
A New Hybrid Bagging Ensemble Machine Learning Model to Identify Parkinson’s Diseases Based on Voice Data
inferred← citeshttps://doi.org/10.1109/cscs59211.2023.00042openalex-snapshot
Review of Metaheuristic Algorithms in Feature Selection based on Parkinson Disease
inferred← citeshttps://doi.org/10.1109/icaihc59020.2023.10431445openalex-snapshot
VADDOT: Vocal Analysis to Detect Diseases of Throat
inferred← citeshttps://doi.org/10.1109/icdsis61070.2024.10594705openalex-snapshot
Early Diagnosis of Parkinson’s Disease Using Deep Learning and Average Voting Classifier
inferred← citeshttps://doi.org/10.1109/icietdw61607.2024.10940000openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icitsif69060.2026.11608628openalex-snapshot
Voice-Based Parkinson’s Disease Detection Using Hybrid Feature Selection and Stacking Ensembles
inferred← citeshttps://doi.org/10.1109/ojcs.2024.3504864openalex-snapshot
Time Series Classification of Raw Voice Waveforms for Parkinson's Disease Detection Using Generative Adversarial Network-Driven Data Augmentation
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HLNBO: Hybrid Leader Namib Beetle Optimization Algorithm-Based LeNet for Classification of Parkinson’s Disease
inferred← citeshttps://doi.org/10.1177/20552076251352941openalex-snapshot
Hybrid preprocessing and ensemble classification for enhanced detection of Parkinson's disease using multiple speech signal databases
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A random forest-based methodology for superior performance in voice-assisted early detection and remote monitoring of Parkinson’s disease
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PARKINSON’S DISEASE CLASSIFICATION USING HYBRID NAMIB SQUIRREL SEARCH WATER ALGORITHM-BASED DEEP LEARNING APPROACH
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Speech and Voice Biomarkers for Parkinson’s disease Detection: A Systematic Review of Machine Learning and Deep Learning Models
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Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review
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Hybrid Machine Learning Framework for Multistage Parkinson’s Disease Classification Using Acoustic Features of Sustained Korean Vowels
inferred← citeshttps://doi.org/10.3390/brainsci15070739openalex-snapshot
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Machine Learning Applications for Diagnosing Parkinson’s Disease via Speech, Language, and Voice Changes: A Systematic Review
inferred← citeshttps://doi.org/10.46889/jcmr.2025.6107openalex-snapshot
AI-Based Quality of Voice Analysis Models for Clinical Use, Insights of Quality of Models from 19 Parkinson’s Disease Studies (2013-2023)
inferred← citeshttps://doi.org/10.64898/2026.01.12.699086openalex-snapshot
Impact of Amazonian Dance on Speech Performance in People with Parkinson’s Disease
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Efficient Parkinson’s disease classification from speech with filter-based feature selection and Genetic Algorithm–Bayesian Optimization ensemble integration
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