Interpretable machine-learning model for cataract associated factors identifying in patients with high myopia

Abstract Purpose To systematically evaluate ocular biometric and systemic laboratory factors associated with cataract in highly myopic eyes and to characterize potential nonlinear associations using an interpretable machine learning approach, thereby providing deeper mechanistic insights into the pathogenesis of highly myopic cataract. Design A cross-sectional study encompassed 770 eyes of 594 patients with high myopia from Eye & ENT Hospital of Fudan University. Subjects The non-cataract control group included 458 eyes while the cataract group contained 312 eyes. Methods Demographic traits, ocular biometric and systemic laboratory factors were gathered while features with over 30% of missing data were excluded. Composite indices were obtained through calculation. Multiple machine learning models were compared to investigate the association between features and highly myopic cataract, and the random forest (RF) model was chosen and fine-tuned. Feature selection was carried out by means of Shapley additive explanations (SHAP) and non-linear relationships were probed using SHAP dependence diagrams and confirmed with partial dependence plots. Main Outcome Measures (1) The Area Under the Curve (AUC) and other metrics of multiple machine learning models; (2) Top feature importance of the final simplified RF model; (3) Overall trends between features and highly myopic cataract; (4) Potential inflection points of top continuous features. Results A simplified fine-tuned RF model with 17 features reached stable discriminative performance, with a mean AUC of 0.762 (95%CI: [0.731, 0.794]) among 10 independent testing sets. Age and axial length (AL) turned out to be the most influential features which had non-linear relationships highly myopic cataract, with an inflection point seen around 65.75 (95%CI: [63.72, 67.79]) years for age and 30.55 (95% CI: [29.22, 31.88]) mm for axial length respectively, while the ratio of anterior chamber depth to axial length (ACD/AL) was associated with highly-myopic cataract in a U-shape. Ocular biometric factors were more strongly related to highly myopic cataract than systemic laboratory factors. Conclusions Ocular biometric factors, especially age, AL, and composite indices like ACD/AL, have strong and non-linear connections with highly myopic cataract. These results emphasize the significance of ocular structural arrangement in cataract within highly myopic eyes and indicate that interpretable data-driven methods could offer clinically relevant understandings regarding its phenotypic description.