Lasso and XGBoost‐Enabled Prediction Models for Sensory Dysfunction, Biological Age, and APOE Genotype in Cognitive Decline Risk Assessment

Our study demonstrates the utility of machine learning-based models in cognitive decline risk assessment, emphasizing the predictive value of sensory dysfunction, biological age, and APOE genotype. These findings underscore the importance of early screening and targeted interventions for at-risk populations. Future research should explore model generalizability across diverse cohorts and incorporate additional biomarkers to enhance precision and clinical applicability.