UNASSIGNED: We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets.
UNASSIGNED: Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75.
UNASSIGNED: The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.
■我们使用机器学习算法来分析语音特征,并开发了用于区分健康对照(HC)和PD患者的诊断模型,并用于区分HCs和轻度PD患者(mH&Y≤1.5)。使用单独的数据集对模型进行独立验证。
■我们的结果表明,该模型在识别轻度PD(mH&Y≤1.5)和HCs患者方面的卓越诊断性能,ROC曲线下面积为0.93(95%CI:0.851.00),准确度0.85,灵敏度0.95,特异性0.75。
■我们的研究结果有助于在缺乏运动障碍专家和特殊设备的社区和基层医疗机构的早期筛查PD。