Parkinson's disease (PD) is a debilitating neurological disorder that necessitates precise and early diagnosis for effective patient care. This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives, a critical factor in clinical practice. Given the limited training data, a feature selection strategy utilizing ANOVA is employed to identify the most informative features. Subsequently, various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector Classifier, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated. The statistical analyses of the results highlight ESN's exceptional performance, showcasing not only superior accuracy but also the lowest false negative rate among all methods. Consistently, statistical data indicates that the ESN method consistently maintains a false negative rate of less than 8% in 83% of cases. ESN's capacity to strike a delicate balance between diagnostic precision and minimizing misclassifications positions it as an exemplary choice for PD diagnosis, especially in scenarios characterized by limited data. This research marks a significant step towards more efficient and reliable PD diagnosis, with potential implications for enhanced patient outcomes and healthcare dynamics.

翻译：帕金森病（PD）是一种致残性神经系统疾病，精准的早期诊断对有效治疗至关重要。本研究旨在开发一种既能实现高诊断准确率又能最大限度降低假阴性率的诊断模型——假阴性率是临床实践中的关键指标。针对训练数据有限的状况，采用基于方差分析的特征选择策略筛选最具信息量的特征。随后，系统评估了包括回声状态网络（ESN）、随机森林、k近邻、支持向量分类器、极限梯度提升和决策树在内的多种机器学习方法。结果统计显著表明：ESN不仅表现出最优的准确率，更在所有方法中实现了最低的假阴性率。持续追踪的统计数据显示，ESN方法在83%的情况下能将假阴性率稳定控制在8%以下。ESN在诊断精度与误分类控制之间实现的精妙平衡，使其成为帕金森病诊断（尤其适用于数据受限场景）的典范选择。本研究标志着向更高效可靠的帕金森病诊断迈出了关键一步，有望改善患者预后并优化医疗资源配置。