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Many existing models lack the ability to capture the nonlinear nature of cognitive decline and individual heterogeneity. This study employed a semi\u2011parametric joint model to analyze longitudinal cognitive trajectories and identify robust predictors of conversion. Data from 596 participants (184 AD converters, 412 stable MCI) were obtained from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Longitudinal assessments included ADAS\u2011Cog13, ADAS\u2011Cog11, CDR\u2011SB, FAQ, RAVLT\u2011IR, RAVLT\u2011L, and MMSE. A semi\u2011parametric joint model combining B\u2011splines for the longitudinal process with a Cox survival submodel was fitted for each cognitive measure. Model performance was evaluated using AIC, BIC, intraclass correlation coefficient (ICC), time\u2011dependent C\u2011index, dynamic AUC, and calibration curves. Subgroup analyses were conducted by APOE\u2011\u03b54 carrier status. In multivariable joint models, APOE\u2011\u03b54 carriage was a consistent risk factor (HR range: 1.38\u20131.77). Higher scores on ADAS\u2011Cog13 (HR\u2009=\u20093.71 per SD), ADAS\u2011Cog11 (HR\u2009=\u20092.71), CDR\u2011SB (HR\u2009=\u20093.79), and FAQ (HR\u2009=\u20092.85) increased the hazard of conversion, whereas higher scores on RAVLT\u2011IR (HR\u2009=\u20090.23), RAVLT\u2011L (HR\u2009=\u20090.14), and MMSE (HR\u2009=\u20090.53) were protective. All models showed high ICCs (0.94\u20130.98) and moderate\u2011to\u2011good predictive accuracy over 2, 5, and 8\u00a0year horizons (C\u2011index: 0.585\u20130.668). CDR\u2011SB and FAQ exhibited the strongest effect sizes and clearest dose\u2011dependent trajectories across APOE\u2011\u03b54 subgroups. Calibration curves demonstrated good agreement between predicted and observed survival. The semi\u2011parametric joint model effectively captures nonlinear cognitive\u2011functional decline and provides validated predictions of AD risk. 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