Abstract

Background:

Parkinson’s disease (PD) is the most prevalent neurodegenerative movement disorder and a growing health concern in demographically aging societies. The prevalence of PD among individuals over the age of 60 and 80 years has been reported to range between 1% and 4% . A timely diagnosis of PD is desirable, even though it poses challenges to medical systems.

Objective:

This study aimed to classify PD and healthy controls based on the analysis of voice records at different frequencies using machine learning (ML) algorithms.

Methods:

The voices of 252 individuals aged 33 to 87 years were recorded. Based on the voice record data, ML algorithms can distinguish PD patients and healthy controls. One binary decision variable was associated with 756 instances and 754 attributes. Voice records data were analyzed through supervised ML algorithms and pipelines. A 10-fold cross-validation method was used to validate models.

Results:

In the classification of PD patients, ML models were performed with 84.21 accuracy, 93 precision, 89 Sensitivity, 89 F1-scores, and 87 AUC. The pipeline performance improved to accuracy: 85.09, precision: 92, Sensitivity:91, F1-score: 89, and AUC: 90. The Pipeline methods improved the performance of classifying PD from voice record.

Conclusions:

Our study demonstrated that ML classifiers and pipelines can classify PD patients based on speech biomarkers. It was found that pipelines were more effective at selecting the most relevant features from high-dimensional data and at accurately classifying PD patients and healthy controls. This approach can therefore be used for early diagnosis of initial forms of PD.

中文翻译：

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使用语音生物标志物对帕金森病患者进行基于机器学习的分类

摘要

背景：

帕金森病 (PD) 是最常见的神经退行性运动障碍，也是人口老龄化社会中日益严重的健康问题。据报道，60 至 80 岁以上人群中 PD 的患病率在 1% 至 4% 之间。尽管 PD 给医疗系统带来了挑战，但及时诊断 PD 仍然是可取的。

客观的：

本研究旨在使用机器学习 (ML) 算法对不同频率的语音记录进行分析，对 PD 和健康对照进行分类。

方法：

252 名年龄在 33 岁至 87 岁之间的人的声音被记录下来。根据语音记录数据，机器学习算法可以区分帕金森病患者和健康对照者。一个二元决策变量与 756 个实例和 754 个属性相关联。通过监督机器学习算法和管道对语音记录数据进行分析。使用10倍交叉验证方法来验证模型。

结果：

在 PD 患者分类中，ML 模型的准确度为 84.21，精确度为 93，灵敏度为 89，F1 分数为 89，AUC 为 87。Pipeline 性能提高到准确度：85.09、精度：92、灵敏度：91、F1 分数：89 和 AUC：90。Pipeline 方法提高了从语音记录中分类 PD 的性能。

结论：

我们的研究表明，机器学习分类器和管道可以根据语音生物标记对 PD 患者进行分类。研究发现，管道在从高维数据中选择最相关的特征以及准确分类 PD 患者和健康对照方面更有效。因此，该方法可用于早期诊断帕金森病的初始形式。