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

Authors information Article notes Copyright and License information

Introduction Material and Methods Results Discussion Conclusions References

Related articles Order reprints Share article Share by email

View HTML version

Figure 8 SHAP value analysis for 24-hour IOP fluctuation prediction models. IOP – intraocular pressure; IOP12pm – intraocular pressure at 12: 00 PM; MAP10am – mean arterial pressure at 10: 00AM; IOP10am – intraocular pressure at 10: 00 AM; CCT – central corneal thickness; RE – refractive error; MOPP10am – mean ocular perfusion pressure at 10: 00 AM; IOP4pm – intraocular pressure at 4: 00 PM; IOP2pm – intraocular pressure at 2: 00 PM; DBP10am – diastolic blood pressure at 10: 00 AM.

Back to the Article

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

Your Privacy