Predicting Early Onset of Age-Related Macular Degeneration: A Machine Learning Approach.

OBJECTIVES Develop a machine learning model that predicts early-onset AMD based on early patient comorbidities DESIGN: Retrospective Case-Control Study SUBJECTS: This study used two datasets: a tertiary eye care center dataset (930 AMD patients, 392 early-onset, 538 late-onset) and the All of Us dataset for validation (560-580 AMD patients early-onset, 560-580 matched controls without AMD). METHODS Health records of AMD patients from a tertiary hospital (1999-2023) and the All of Us Research Program, a nationwide longitudinal cohort of U.S. adults (data collected 6/2016-2/2025) were collected. Unsupervised clustering using Uniform Manifold Approximation and Projection (UMAP) was performed on the tertiary eye care center dataset to identify distinct patient subgroups. Supervised machine learning models, including gradient-boosted decision trees (GBDT), logistic regression, and random forests, were trained to predict early-onset AMD. MAIN OUTCOMES MEASURES Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), while feature importance was assessed using the Gini index for tree-based models and coefficient magnitudes for logistic regression. Additionally, odds ratios for comorbidities associated with early-onset AMD were estimated using 2 × 2 contingency tables in the validation dataset (All of Us). RESULTS Clustering identified two distinct patient groups, with one (93.08% early-onset AMD dominated) showing high rates of inflammatory joint disorders, hypertension, and dyslipidemia. Multiple supervised machine learning models achieved ∼76% accuracy in predicting early-onset AMD from early comorbidities (diagnosed before age of 55). Like clustering, analysis of supervised machine learning models identified inflammatory joint disorders, hypertension, and dyslipidemia as the most predictive features. These comorbidities were validated using the All of Us dataset, with essential hypertension (OR: 5.80, 95% CI: [4.25-7.93], p < 0.05), rheumatoid arthritis (OR: 4.61, 95% CI: [1.55-13.72], p < 0.05), and hyperlipidemia (OR: 5.16, 95% CI: [3.76-7.08], p < 0.05) showing strong associations with early-onset AMD. CONCLUSION Screening hypertension, dyslipidemia, and inflammatory joint disorders in ophthalmologic evaluations may enable earlier AMD detection and intervention.