NEUROCOG-DysNet: a multi-scale cognitive feature engineering framework with stacking ensemble for improved dyslexia screening.

Introduction: Dyslexia affects around 10% of the global population, yet early screening depends heavily on resource-intensive clinical assessments that are difficult to scale. Gamified cognitive platforms offer a practical alternative, but most existing machine learning approaches rely on raw task metrics without exploiting the hierarchical cognitive structure of multi-task assessment batteries. Methods: NEUROCOG-DysNet is a multi-scale cognitive feature engineering and stacking ensemble framework for dyslexia screening using gamified assessment data from the Dytective platform ( Results: The stacking meta-learner achieves the highest composite score and attains an AUC-ROC of 0.8924 (95% CI: 0.8543-0.9253), sensitivity of 0.8077, specificity of 0.8187, and balanced accuracy of 0.8132. DeLong's test ( Discussion: NEUROCOG-DysNet provides a tunable sensitivity-specificity balance and well-calibrated risk scores suitable for a first-stage screening aid. The ensemble's advantage over fixed-weight alternatives is marginal on this dataset, and the cross-domain feature level is effectively inert. External validation across independent language cohorts and prospective multi-center evaluation are required before clinical deployment.