A Dynamic Composite Ensemble Learning Framework for Multi-Stage Dementia Prediction

Dementia has increasingly impacted the health of older adults, necessitating precise prediction of cognitive levels to deliver targeted healthcare services and mitigate disease progression. In recent years, although there has been surging interest in research on AI-driven dementia prediction, existing methods commonly rely on background-specific datasets and utilize multi-models with equal weights for predictions. These methods limit the result to the inherent features of the datasets on the one hand and ignore the differences among various feature channels on the other hand. To address these limitations, this paper proposes a dynamic composite ensemble learning framework, including the following three novel modules. Firstly, based on the selected common feature set, we construct a top-level feature set through dynamically determined screening thresholds. Subsequently, we deploy transfer learning models for knowledge complementation to enhance model generalization. In addition, we purposefully designed a dynamic weighting module to prioritize feature channels with stronger relevance to the target task. We deploy our model on the ELSA-HCAP dataset and conduct a series of experiments to evaluate its practical effectiveness. The results demonstrate that the proposed feature engineering, dynamic weighting, and knowledge transfer modules collectively enhance the overall model performance. Moreover, the combination of these three modules in the fusion model attains optimal performance, achieving an accuracy rate of 95.76%.