Predicting the functional outcomes of anti-LGI1 encephalitis using a random forest model

Severity Identification of Graves Orbitopathy via Random Forest Algorithm

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.