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This study aims to determine the effectiveness of machine learning models in detecting Parkinson’s Disease using voice acoustic features.

July 1, 2026BioMedOpen Access

Identification of Parkinson’s Disease from Native Italian People: Machine Learning Voice Analysis

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This study aims to determine the effectiveness of machine learning models in detecting Parkinson’s Disease using voice acoustic features.
Utilized the Italian Parkinson’s Voice and Speech dataset for model training and validation.
Extracted various acoustic features including MFCCs and GTCCs for analysis.
Employed six supervised classifiers for performance evaluation.
SVM model achieved 94.68% accuracy with 95.04% specificity and 96.04% F1-score.
MLP classifier yielded 93.08% accuracy with 91.18% specificity and an ROC-AUC of 0.97.
Demonstrated strong potential for speech as a biomarker for early Parkinson’s Disease detection.

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Randomized trial identifies early markers of Parkinson’s Disease in native Italians, suggesting use of voice analysis for diagnosis.

Hossain et al. (2026) studied this question.

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