Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China.
Department of Ophthalmology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Horm Metab Res. 2024 Oct;56(10):706-711. doi: 10.1055/a-2287-3734. Epub 2024 Apr 8.
This study aims to establish a random forest model for detecting the severity of Graves Orbitopathy (GO) and identify significant classification factors. This is a hospital-based study of 199 patients with GO that were collected between December 2019 and February 2022. Clinical information was collected from medical records. The severity of GO can be categorized as mild, moderate-to-severe, and sight-threatening GO based on guidelines of the European Group on Graves' orbitopathy. A random forest model was constructed according to the risk factors of GO and the main ocular symptoms of patients to differentiate mild GO from severe GO and finally was compared with logistic regression analysis, Support Vector Machine (SVM), and Naive Bayes. A random forest model with 15 variables was constructed. Blurred vision, disease course, thyroid-stimulating hormone receptor antibodies, and age ranked high both in mini-decreased gini and mini decrease accuracy. The accuracy, positive predictive value, negative predictive value, and the F1 Score of the random forest model are 0.83, 0.82, 0.86, and 0.82, respectively. Compared to the three other models, our random forest model showed a more reliable performance based on AUC (0.85 vs. 0.83 vs. 0.80 vs. 0.76) and accuracy (0.83 vs. 0.78 vs. 0.77 vs. 0.70). In conclusion, this study shows the potential for applying a random forest model as a complementary tool to differentiate GO severity.
本研究旨在建立一个随机森林模型来检测 Graves 眼病（GO）的严重程度并识别重要的分类因素。这是一项基于医院的研究，共纳入了 199 例 2019 年 12 月至 2022 年 2 月期间就诊的 GO 患者。从病历中收集临床信息。GO 的严重程度可根据欧洲 Graves 眼病小组的指南分为轻度、中重度和威胁视力的 GO。根据 GO 的危险因素和患者主要眼部症状构建随机森林模型，以区分轻度 GO 和重度 GO，最后与逻辑回归分析、支持向量机（SVM）和朴素贝叶斯进行比较。构建了一个包含 15 个变量的随机森林模型。视力模糊、病程、促甲状腺激素受体抗体和年龄在 mini-decreased gini 和 mini decrease accuracy 中排名均较高。随机森林模型的准确率、阳性预测值、阴性预测值和 F1 评分分别为 0.83、0.82、0.86 和 0.82。与其他三个模型相比，我们的随机森林模型在 AUC（0.85 对 0.83 对 0.80 对 0.76）和准确率（0.83 对 0.78 对 0.77 对 0.70）方面表现出更可靠的性能。总之，本研究表明，随机森林模型有可能作为一种补充工具来区分 GO 的严重程度。
