Validating the Usefulness of the "Random Forests" Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography
Validating the Usefulness of the "Random Forests" Classifier to Diagnose Early Glaucoma With Optical Coherence Tomography
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DOI：
10.1016/j.ajo.2016.11.001
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发表时间：
2017-02-01
影响因子：
4.2
通讯作者：
Araie, Makoto
中科院分区：
文献类型：
--
作者：
Asaoka, Ryo;Hirasawa, Kazunori;Araie, Makoto
目的：验证“随机森林”分类器在光谱域光学相干断层扫描（SDOCT）诊断早期青光眼中的有效性。方法：设计：比较诊断算法。环境：多个机构实践。研究对象：训练数据集包括94例开角型青光眼患者的94只眼和84例正常人的84只眼，测试数据集包括114例开角型青光眼患者的114只眼和82例正常人的82只眼。两组均纳入平均偏差（MD）值大于5.0 dB的OAG眼。观察过程：使用训练数据集，使用随机森林方法，基于反向或双向逐步模型选择的多重逻辑回归模型，最小绝对收缩和选择算子回归（LASSO）模型和Ridge回归模型，建立分类器来区分青光眼和正常眼睛。主要观察指标：诊断准确性。结果：在检验数据中，随机森林法的受试者工作特征曲线（AROC）下面积（93.0%）显著（P < 0.05）大于逐步模型选择法（71.9%）、LASSO模型（89.6%）和Ridge模型（89.2%）。结论：随机森林法同时分析多个SDOCT参数对青光眼早期诊断有一定的价值。(C) 2016 Elsevier Inc.版权所有。
PURPOSE: To validate the usefulness of the "Random Forests" classifier to diagnose early glaucoma with spectral-domain optical coherence tomography (SDOCT).METHODS: DESIGN: Comparison of diagnostic algorithms. SETTING: Multiple institutional practices. STUDY PARTICIPANTS: Training dataset included 94 eyes of 94 open-angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects and testing dataset included 114 eyes of 114 OAG patients and 82 eyes of 82 normal subjects. In both groups, OAG eyes with mean deviation (MD) values better than 5.0 dB were included. OBSERVATION PROCEDURE: Using the training dataset, classifiers were built' to discriminate between glaucoma and normal eyes using 84 OCT measurements using the Random Forests method, multiple logistic regression models based on backward or bidirectional stepwise model selection, a least absolute shrinkage and selection operator regression (LASSO) model, and a Ridge regression model. MAIN OUTCOME MEASURES: Diagnostic accuracy.RESULTS: With the testing data, the area under the receiver operating characteristic curve (AROC) with the Random Forests method (93.0%) was significantly (P < .05) larger than those with other models of the stepwise model selections " (71.9%), LASSO model (89.6%), and Ridge model (89.2%).CONCLUSION: It is useful to analyze multiple SDOCT parameters concurrently using the Random Forests method to diagnose glaucoma in early stages. (C) 2016 Elsevier Inc. All rights reserved.