Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study

Chen Ranran; Lei Jinming; Liao Yujie; Jin Yiping; Wang Xue; Li Hong; Bi Yanlong; Zhu Haohao

doi:10.12659/MSM.945483

03 December 2024 : Clinical Research

Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study

Chen Ranran^12ACDEG, Lei Jinming^3ACD, Liao Yujie^12BC, Jin Yiping^12BC, Wang Xue^4CD, Li Hong^1BD, Bi Yanlong^5ACF*, Zhu Haohao^12ABFG

DOI: 10.12659/MSM.945483

Med Sci Monit 2024; 30:e945483

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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.

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Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative Study

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