The Effect of Speech Fragmentation and Audio Encodings on Automatic Parkinson’s Disease Recognition

Schiferl, D.; Jenei, Attila Zoltán; Valálik, István; Vicsi, Klára

doi:10.4236/jbise.2022.151002

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Consistent with the literature (16, 32,33,42), our ndings a rm the superior performance of SVM models, aligning with the trends in PD diagnosis using voice analysis. Only a few studies showed different results (43) (44).…”

Section: Discussionsupporting

confidence: 91%

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comprehensive Study Across Diverse Datasets

Neto

2023

SSRN Journal

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Section: Discussionsupporting

confidence: 91%

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comprehensive Study Across Diverse Datasets

Neto

2023

SSRN Journal

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“…As for the parameters tested, for the DNN, we explored the number of neurons in the rst dense layer (16,32,64), learning rates (0.001, 0.01, 0.05), dropout rates (0.1, 0.3, 0.5), batch sizes (8,16,32), epochs (30,70), activation functions (sigmoid, tanh, relu, and swish) and optimizers (Adaptive Moment Estimation (Adam), Stochastic Gradient Descent and Adamax, a variant of Adam based on the in nity norm). For the GB classi er, we adjusted several hyperparameters: learning rate (0.1, 0.2, 0.3), max depth (5), max features (sqrt, log2), min samples leaf (10, 20), min samples split (20,30), n_estimators (200, 300), and subsample (0.8, 0.9).…”

Section: Machine Learning Analysismentioning

confidence: 99%

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets

Neto

2024

Preprint

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Objective: This study evaluates the efficacy of voice analysis combined with machine learning (ML) techniques in enabling early, noninvasive diagnosis of Parkinson's Disease (PD). Methods: Voice data, phonation of the vowel 'a', from three distinct datasets (two from the UCI ML Repository and one from figshare) for a total of 432 participants (278 PD patients) were analyzed. We employed four ML models - Artificial Neural Networks (ANN), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) - alongside two ensemble methods (soft voting classifier - EVC and stacking method - ESM). The models underwent 50 iterations of evaluation, involving various data splits and 10-fold cross-validation. Comparative analysis was done using one-way ANOVA followed by Bonferroni post-hoc corrections. Results: The ESM, SVM, and GB models emerged as the top performers, demonstrating superior performance across metrics including accuracy, sensitivity, specificity, precision, F1 score, and ROC AUC. Despite data heterogeneity and variable selection limitations, the models showed high values for all metrics. Conclusion: Machine learning integration with voice analysis, mainly through ESM, SVM, and GB, is promising for early PD diagnosis. Using multi-source data and a large sample size enhances our findings' validity, reliability, and generalizability. Significance: This study underscores the potential of noninvasive voice analysis combined with advanced ML to revolutionize early PD detection and pave the way for cost-effective, scalable diagnostic tools.

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Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets

Neto

2024

Journal of Voice

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The Effect of Speech Fragmentation and Audio Encodings on Automatic Parkinson’s Disease Recognition

Cited by 5 publications

References 24 publications

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comprehensive Study Across Diverse Datasets

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comprehensive Study Across Diverse Datasets

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets

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