Journal article
Predicting Intraocular Pressure From Glaucoma Patients Receiving Medication Treatment Using Explainable Machine Learning
BioMed research international, v 2026(1), 9930837
01 Jan 2026
PMID: 41623694
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Glaucoma is a chronic neurodegenerative disease of the visual system, and treatment is targeted toward lowering intraocular pressure. However, some patients fail to respond to treatment and their intraocular pressure levels remain high, risking continuous vision loss. Explainable machine learning provides a mechanism for both individual prognostication and the identification of factors associated with treatment outcome. Here, we used explainable machine learning to predict intraocular pressure for glaucoma patients receiving medication treatment. We accessed the UK Biobank to obtain information on 290 eyes from 161 participants who reported a diagnosis of glaucoma and were receiving treatment. Features were divided into three distinct datasets containing demographic data only, physiometabolic parameters and medication prescription data, and all data combined. We evaluated five machine learning techniques for each feature set in terms of their ability to predict intraocular pressure at a follow-up visit in a classification task. We then calculated SHapley Additive exPlanation (SHAP) values for the best performing model to determine feature importance, stability, and interactions. We found that eXtreme Gradient Boosting (XGBoost) outperformed all other models when trained and tested on the combined feature set with an area under receiver operating characteristic curve (AUC) of 0.708. Insulin-like growth factor 1 (IGF-1), low-density lipoprotein (LDL), and lymphocyte count ranked as the three most important features for this model. LDL and IGF-1 exhibited a low degree of global variability in contribution to the model output across all cross-validation repeats. SHAP values demonstrated the strongest interactions being between LDL and IGF-1. In summary, our studies indicated the importance of blood LDL and IGF-1 in contributing to the outcomes of intraocular pressure lowering treatment and demonstrated the ability of XGBoost to predict these outcomes.
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Details
- Title
- Predicting Intraocular Pressure From Glaucoma Patients Receiving Medication Treatment Using Explainable Machine Learning
- Creators
- Robert T James - New York UniversityWenke Liu - New York UniversityGadi Wollstein - Drexel University School of Biomedical Engineering, Science and Health Systems, Philadelphia, Pennsylvania, USAJoel S Schuman - Drexel University School of Biomedical Engineering, Science and Health Systems, Philadelphia, Pennsylvania, USADavid Fenyo - New York UniversityKevin C Chan - Spencer Center for Vision Research, Byers Eye Institute, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, California, USA, stanford.edu
- Publication Details
- BioMed research international, v 2026(1), 9930837
- Grant note
- R01 EY013178 / NEI NIH HHS R01 EY028125 / NEI NIH HHS R01 EY036070 / NEI NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:001674800500001
- Scopus ID
- 2-s2.0-105028979876
- Other Identifier
- 991022170439004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biotechnology & Applied Microbiology
- Medicine, Research & Experimental