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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Table 2 Comparison of methods.

	Accuracy	Specifity	10-fold cross-validation	Precision	Sensitivity	F1-score
SVM	0.777	0.944	0.744	0.500	0.195	0.281
LR	0.777	0.937	0.744	0.500	0.220	0.305
XGBoost	0.886	0.972	0.872	0.857	0.585	0.696
KNN	0.831	0.930	0.793	0.667	0.488	0.563
Naive Bayes	0.712	0.797	0.671	0.415	0.370	0.391
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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