Neurodegenerative conditions including dementia are a leading health challenge worldwide, and more often than not, progress implicitly, in a manner that they cannot be detected until very late in the disease. Risk prediction at an early stage thus becomes very essential to allow the provision of individualized care and even to curb cognitive impairment. The study proposes a multi-modal, federated AI and hierarchical attention  framework for  integrating clinical assessment, neuroimaging, genetic/omics biomarkers, digital behavior data and longitudinal electronic health records over 5-10 years across multi- center cohorts. The modalities are encoded by specialized deep learning decoders such as CNNs for imaging and clinical data, Transformers for behavioral data and graph neural networks for genetic/omic inputs. The modality-dependent embeddings get integrated by hierarchical attention operation to introduce trajectory-aware representations that realize temporal patient dynamics. Dynamic risk scores and time-to-event estimates are produced in a model-based optimization framework using recurrent neural networks and survival-transformers. The evaluation demonstrates robust performance with an AUC-ROC of 93.8 percent (95 percent CI) and a C-index of 0.87. The framework increases the predictive performance, interpretability, and privacy, and provides a clinically deployable tool of early dementia risk profiling and intervention planning.

Dementia Risk Prediction; Multi-Modal Data Integration; federated learning; Survival Analysis; Neuroimaging; Genetic Biomarkers; Explainability