Non-invasive detection of Parkinson's disease based on speech analysis and interpretable machine learning.
Objective: Parkinson's disease (PD) is a progressive neurodegenerative disorder that significantly impacts motor function and speech patterns. Early detection of PD through non-invasive methods, such as speech analysis, can improve treatment outcomes and quality of life for patients. This study aims...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 12 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185162506&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185162506 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2025 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 185162506 185162506 185162506 10.3389/fnagi.2025.1586273 185162506 ppf: 1 ppct: 11 formats: tig: atl: Non-invasive detection of Parkinson's disease based on speech analysis and interpretable machine learning. aug: au: Xu, Huanqing Xie, Wei Pang, Mingzhen Li, Ya Jin, Luhua Huang, Fangliang Shao, Xian affil: The School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China sug: subj: Parkinson Disease Diagnosis Speech Evaluation Machine Learning Methods Diagnosis, Computer Assisted Methods Noninvasive Procedures User-Computer Interface Predictive Value of Tests Evaluation Human Artificial Intelligence Information Science Exploratory Research Descriptive Statistics Random Forest Support Vector Machine Neural Networks (Computer) ROC Curve Mann-Whitney U Test Pearson's Correlation Coefficient Funding Source ab: Objective: Parkinson's disease (PD) is a progressive neurodegenerative disorder that significantly impacts motor function and speech patterns. Early detection of PD through non-invasive methods, such as speech analysis, can improve treatment outcomes and quality of life for patients. This study aims to develop an interpretable machine learning model that uses speech recordings and acoustic features to predict PD. Methods: A dataset of speech recordings from individuals with and without PD was analyzed. The dataset includes features such as fundamental frequency (Fo), jitter, shimmer, noise-to-harmonics ratio (NHR), and non-linear dynamic complexity measures. Exploratory data analysis (EDA) was conducted to identify patterns and relationships in the data. The dataset was split into 70% training and 30% testing sets. To address class imbalance, synthetic minority oversampling technique (SMOTE) was applied. Several machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forests, and Neural Networks, were implemented and evaluated. Model performance was assessed using accuracy, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) metrics. SHapley Additive exPlanations (SHAP) were used to explain the models and evaluate feature contributions. Results: The analysis revealed that features related to speech instability, such as jitter, shimmer, and NHR, were highly predictive of PD. Non-linear metrics, including Recurrence Plot Dimension Entropy (RPDE) and Pitch Period Entropy (PPE), also made significant contributions to the model's predictive power. Random Forest and Gradient Boosting models achieved the highest performance, with an AUC-ROC of 0.98, recall of 0.95, ensuring minimal false negatives. SHAp values highlighted the importance of fundamental frequency variation and harmonic-to-noise ratio in distinguishing PD patients from healthy individuals. Conclusion: The developed machine learning model accurately predicts Parkinson's disease using speech recordings, with Random Forest and Gradient Boosting algorithms demonstrating superior performance. Key predictive features include jitter, shimmer, and non-linear dynamic complexity measures. This study provides a reliable, non-invasive tool for early PD detection and underscores the potential of speech analysis in diagnosing neurodegenerative diseases. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|