03 December 2024 : Clinical Research
Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study
Chen Ranran ACDEG 1,2, Lei Jinming ACD 3, Liao Yujie BC 1,2, Jin Yiping BC 1,2, Wang Xue CD 4, Li Hong BD 1, Bi Yanlong ACF 5*, Zhu Haohao ABFG 1,2DOI: 10.12659/MSM.945483
Med Sci Monit 2024; 30:e945483
Figure 6 Precision-Recall curves and AUCPR results for predicting 24-hour IOP fluctuations. (A) The 5 algorithms, P<1; (B) The 5 algorithms, P<0.1; (C) The 5 algorithms, P<0.05; (D) The 5 algorithms, P<0.025. IOP, intraocular pressure; SVM – support vector machines; LR – Logistic Regression; XGBoost – Extreme Gradient Boosting; KNN – K-Nearest Neighbors; Naive Bayes – Naive Bayes Classifier.