Xu Huanqing, Xie Wei, Pang Mingzhen, Li Ya, Jin Luhua, Huang Fangliang, Shao Xian
The School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Front Aging Neurosci. 2025 Apr 30;17:1586273. doi: 10.3389/fnagi.2025.1586273. eCollection 2025.
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.
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.
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.
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.
帕金森病（PD）是一种进行性神经退行性疾病，对运动功能和言语模式有显著影响。通过语音分析等非侵入性方法早期检测帕金森病，可以改善患者的治疗效果和生活质量。本研究旨在开发一种可解释的机器学习模型，该模型利用语音记录和声学特征来预测帕金森病。
分析了有和没有帕金森病个体的语音记录数据集。该数据集包括基频（Fo）、抖动、闪烁、噪声与谐波比（NHR）以及非线性动态复杂性度量等特征。进行探索性数据分析（EDA）以识别数据中的模式和关系。数据集被分为70%的训练集和30%的测试集。为了解决类别不平衡问题，应用了合成少数过采样技术（SMOTE）。实施并评估了几种机器学习算法，包括K近邻（KNN）、支持向量机（SVM）、决策树、随机森林和神经网络。使用准确率、召回率、F1分数和接收器操作特征曲线下面积（AUC-ROC）指标评估模型性能。使用SHapley加性解释（SHAP）来解释模型并评估特征贡献。
分析表明，与语音不稳定性相关的特征，如抖动、闪烁和NHR，对帕金森病具有高度预测性。包括递归图维度熵（RPDE）和基音周期熵（PPE）在内的非线性度量也对模型的预测能力做出了重大贡献。随机森林和梯度提升模型表现最佳AUC-ROC为0.98，召回率为0.95，确保了最小的假阴性。SHAP值突出了基频变化和谐波与噪声比在区分帕金森病患者和健康个体方面的重要性。
所开发的机器学习模型使用语音记录准确预测帕金森病，随机森林和梯度提升算法表现出卓越性能。关键预测特征包括抖动、闪烁和非线性动态复杂性度量。本研究为帕金森病的早期检测提供了一种可靠的非侵入性工具，并强调了语音分析在诊断神经退行性疾病方面的潜力。
