Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest
Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest
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DOI：
10.3389/fnins.2020.00798
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发表时间：
2020-08-06
影响因子：
4.3
通讯作者：
Chen, Xueshuo
中科院分区：
文献类型：
--
作者：
Mao, Yuxing;He, Yinghong;Chen, Xueshuo
医学研究表明，眼动障碍与多种神经系统疾病有关。眼动特征可以作为帕金森病、阿尔茨海默病（AD）、精神分裂症和其他疾病的生物标志物。然而，由于一些疾病的医学机制尚不清楚，很难在眼动特征与疾病之间建立直观的对应关系。本文提出了一种基于决策树和随机森林的疾病分类方法。首先，设计多种实验方案获取眼球运动图像，提取瞳孔位置、面积等信息作为原始特征；其次，以原始特征作为训练样本，利用长短期记忆（LSTM）网络构建分类器，将样本的分类结果作为进化特征；然后，根据进化特征，根据C4.5规则构建多棵决策树。最后，利用这些决策树构造一个决策树，并通过投票确定疾病分类的结果。实验表明，该方法具有较好的鲁棒性，分类精度明显优于以往的分类器。本研究表明，将先进的人工智能（AI）技术应用于眼动病理分析具有明显的优势和良好的前景。
Medical research shows that eye movement disorders are related to many kinds of neurological diseases. Eye movement characteristics can be used as biomarkers of Parkinson's disease, Alzheimer's disease (AD), schizophrenia, and other diseases. However, due to the unknown medical mechanism of some diseases, it is difficult to establish an intuitive correspondence between eye movement characteristics and diseases. In this paper, we propose a disease classification method based on decision tree and random forest (RF). First, a variety of experimental schemes are designed to obtain eye movement images, and information such as pupil position and area is extracted as original features. Second, with the original features as training samples, the long short-term memory (LSTM) network is used to build classifiers, and the classification results of the samples are regarded as the evolutionary features. After that, multiple decision trees are built according to the C4.5 rules based on the evolutionary features. Finally, a RF is constructed with these decision trees, and the results of disease classification are determined by voting. Experiments show that the RF method has good robustness and its classification accuracy is significantly better than the performance of previous classifiers. This study shows that the application of advanced artificial intelligence (AI) technology in the pathological analysis of eye movement has obvious advantages and good prospects.