目的:本研究评估了语音分析结合机器学习(ML)技术在帕金森病(PD)诊断中的功效。
方法:语音数据,元音\“a”的发声,“从三个不同的数据集(两个来自加州大学欧文分校ML存储库,一个来自无花果)对432名参与者(278名PD患者)进行了分析。我们采用了四种机器学习模型-人工神经网络,随机森林,梯度提升(GB),和支持向量机(SVM)-以及两种集成方法(软投票分类器-集成投票分类器和堆叠方法-集成堆叠模型(ESM))。这些模型经历了50次迭代评估,涉及各种数据分割和10倍交叉验证。使用单向方差分析进行比较分析,然后进行Bonferronipostthoc校正。
结果:ESM,SVM,GB模型成为表现最好的,展示跨指标的卓越性能,包括准确性,灵敏度,特异性,精度,F1得分,和受试者工作特征曲线下面积(ROCAUC)。尽管数据异质性和变量选择限制,模型对所有指标都显示出较高的值.
结论:ML与语音分析的集成,主要通过ESM,SVM,GB,对早期PD诊断很有希望。使用多源数据和大样本量提高了我们的发现的有效性,可靠性,和普适性。
结论:将先进的ML技术与语音分析相结合,显示出改善早期PD检测的巨大潜力,为语言病理学家(SLP)提供有价值的工具。这些发现提供了临床相关的见解,可以在SLP实践的范围内应用,以完善诊断过程并促进早期干预。
OBJECTIVE: This
study evaluates the efficacy of voice analysis combined with machine learning (ML) techniques in enabling the diagnosis of Parkinson\'s disease (PD).
METHODS: Voice data, phonation of the vowel \"a,\" from three distinct datasets (two from the University of California Irvine ML Repository and one from figshare) for 432 participants (278 PD patients) were analyzed. We employed four ML models-Artificial Neural Networks, Random Forest, Gradient Boosting (GB), and Support Vector Machine (SVM)-alongside two ensemble methods (soft voting classifier-Ensemble Voting Classifier and stacking method-Ensemble Stacking Model (ESM)). The models underwent 50 iterations of evaluation, involving various data splits and 10-fold cross-validation. Comparative analysis was done using one-way Analysis of Variance followed by Bonferroni posthoc 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 area under the receiver operating characteristic curve (ROC AUC). Despite data heterogeneity and variable selection limitations, the models showed high values for all metrics.
CONCLUSIONS: ML 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.
CONCLUSIONS: Integrating advanced ML techniques with voice analysis demonstrates substantial potential for improving early PD detection, offering valuable tools for speech-language pathologists (SLPs). These findings provide clinically relevant insights that can be applied within the scope of SLP practice to refine diagnostic processes and facilitate early intervention.