Abstract
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.
Keywords
Graves orbitopathy - severity - random forest algorithm
