AlzDetect: A Heterogeneous Framework for Early Alzheimer Detection Using Unified Multimodal Indicators

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early risk identification is essential for timely clinical assessment, care planning, and long-term disease management. However, computational modeling of AD remains challenging because clinically meaningful diagnosis cannot be inferred from numerical separability alone; it requires alignment with clinical assessment and biomarker validation. From a computational social systems perspective, a major challenge is the heterogeneity and fragmentation of patient-related information, including wearable actigraphy signals, clinical records, and demographic contexts. To address this challenge, we propose AlzDetect, an enhanced TabNet-based deep learning (DL) framework for screening-oriented AD risk modeling across clinically grounded and wearable-derived data domains. AlzDetect is not presented as a subject-level multimodal diagnostic system; rather, it is evaluated as a heterogeneous data risk stratification framework under domain shift, missingness, and cross-domain robustness settings. The wearable-derived (Google Pixel Watch 2) dataset is used only for feasibility and robustness analysis of behavioral signal modeling, not as biomarker-confirmed evidence for AD diagnosis or clinical staging. AlzDetect provides transparent decision support through TabNet’s sparse attention masks, which identify influential predictors at feature and modality levels. The extensive evaluation shows that AlzDetect achieves 96% predictive accuracy and outperforms representative baselines within the evaluated dataset specific settings, demonstrating strong predictive separability and robustness across heterogeneous AD relevant data sources.