Predicting 24-hour intraocular pressure peaks and averages with machine learning - Chen, Ranran; Lei, Jinming; Liao, Yujie; Jin, Yiping; Wang, Xue; Li, Xiaomei; Wu, Danping; Li, Hong; Bi, Yanlong; Zhu, Haohao (Frontiers in Medicine, 2024)
Predicting 24-hour peak and average intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma. This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. We also aimed to identify relevant features and provide insights into the prediction results to better inform clinical practice.In this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. Five time points (10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM) were tested to optimize prediction accuracy using their combinations. The model with the highest performance was selected, and feature importance was assessed using Shapley Additive Explanations.