Background

Parkinson's disease (PD) is a neurodegenerative disease of the elderly, which leads to patients’ motor and non-motor disabilities and affects patients’ quality of daily life. Timely and effective detection of PD is a key step to medical intervention. Recently, computer aided methods for PD detection have gained lots of attention in artificial intelligence domain.

Methods

This paper proposed a novel ensemble learning model fusing Random Forest (RF) classifiers and Principal Component Analysis (PCA) technique to differentiate PD patients from healthy controls (HC). Six different RF models were separately constructed to generate the corresponding class probability vectors which represent an individual’s category predictions on 6 different handwritten exams, and the final prediction result for an individual was obtained through voting strategy of all RF models. Stratified k-fold cross validation was performed to split the exam datasets and evaluate the classification performances.

Results

Experimental results prove that our proposed ensemble model on six handwritten exams has achieved better classification performances than a single RF based method on a single handwritten exam. Our ensemble of RF model based on multiple handwritten exams has promising accuracy (89.4 %), specificity (93.7 %), sensitivity (84.5 %) and F1-score (87.7 %). Compared with Logistic Regression (LR) and Support Vector Machines (SVM), the ensemble model based on RF can achieve better classification results.

Conclusion

A computer-assisted PD diagnosis model on small handwritten dynamics dataset is proposed, and it provides a potential way for assisting diagnosis of PD in clinical setting.

中文翻译：

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一种新的随机森林集成体，可在小型手写动态数据集上辅助诊断帕金森氏病

背景

帕金森氏病（PD）是老年人的神经退行性疾病，会导致患者的运动和非运动障碍，并影响患者的日常生活质量。及时有效地检测PD是医疗干预的关键步骤。近年来，用于PD检测的计算机辅助方法在人工智能领域引起了广泛关注。

方法

本文提出了一种新的集成学习模型，融合了随机森林（RF）分类器和主成分分析（PCA）技术，以区分PD患者与健康对照（HC）。分别构建了六个不同的RF模型，以生成相应的类别概率矢量，这些矢量代表了6个不同的手写考试上的个人类别预测，并且通过所有RF模型的投票策略获得了个人的最终预测结果。进行了分层的k折交叉验证，以拆分检查数据集并评估分类性能。

结果

实验结果证明，我们提出的六项手写考试的集成模型比单项手写考试的基于RF的方法具有更好的分类性能。我们基于多次手写检查的RF模型集合具有令人鼓舞的准确性（89.4％），特异性（93.7％），敏感性（84.5％）和F1评分（87.7％）。与Logistic回归（LR）和支持向量机（SVM）相比，基于RF的集成模型可以实现更好的分类结果。

结论

提出了一种基于小型手写动态数据集的计算机辅助PD诊断模型，为临床诊断PD提供了一种潜在的途径。