Unveiling the non-linear synergistic effects of smoking and aging on cataract-AMD comorbidity: an explainable artificial intelligence approach.

BACKGROUND

The comorbidity of cataract and age-related macular degeneration (AMD) poses a significant public health burden. Traditional linear statistical models often fail to capture complex, non-linear interactions among risk factors. This study aimed to develop an interpretable machine learning framework to predict comorbidity risk and elucidate the synergistic effects of systemic and ocular factors.

METHODS

A retrospective case-control study was conducted involving 640 participants (264 comorbidity cases and 376 controls). Fifteen multi-dimensional clinical features were extracted. Four machine learning algorithms-Logistic Regression, Random Forest, SVM, and XGBoost-were trained and validated. Model performance was assessed via AUROC, AUPRC, and calibration curves. SHapley Additive exPlanations (SHAP) and LIME were employed to visualize global and local interpretability.

RESULTS

The XGBoost model demonstrated robust discriminative performance (AUC = 0.895, 95% CI: 0.85-0.93) and calibration compared to other algorithms. SHAP analysis identified drusen severity and lens opacity (LOCS III) as dominant ocular predictors, while C-reactive protein (CRP) and smoking were critical systemic contributors. Notably, interaction analysis revealed a non-linear synergistic effect: smoking was associated with an exponentially higher comorbidity risk in individuals aged >75 years, whereas CRP exhibited a distinct saturation threshold effect. Decision curve analysis confirmed the model's high net clinical benefit across a wide range of threshold probabilities.

CONCLUSION

This study establishes a robust, clinically applicable risk stratification tool for cataract and AMD comorbidity. By uncovering non-linear interactions between aging, lifestyle, and inflammation, it provides valuable evidence-based support for personalized screening and preventive intervention.