Deep Learning Models for Early Detection of Chronic Diseases Using Multimodal Healthcare Data

Abstract

Early detection of chronic diseases is critical for improving patient outcomes and reducing healthcare burdens. The increasing availability of heterogeneous healthcare data, including electronic health records, medical imaging, and wearable sensor measurements, offers unprecedented opportunities for predictive modeling, yet poses significant challenges in integration and interpretation. In this study, we develop a multimodal deep learning framework that combines structured EHR data, time-series signals from wearable devices, and medical images to predict the onset of chronic conditions. Our methodology incorporates attention-based fusion mechanisms and hybrid architectures, including CNN-LSTM networks, to capture both spatial and temporal patterns across modalities. Extensive evaluations demonstrate that the multimodal approach substantially outperforms unimodal models, yielding robust, interpretable predictions even in the presence of missing or noisy data. The results highlight the potential of integrating diverse healthcare data streams to enable timely clinical interventions. This work contributes a systematic framework for multimodal disease prediction, demonstrating practical strategies for model development, ablation analysis, and sensitivity testing in complex real-world healthcare scenarios.