HealthEngine: An Integrated Healthcare Analytics Model Using Multimodal Transformer, Deep Multitask Neural Networks, and SHAP

The development of more accurate and explainable health predictions is paramount in view of the increasing prevalence rates of chronic diseases and mental illnesses. Existing methods often under-utilize the rich, heterogeneous data streams coming from wearable devices, environmental sensors, and behavioral data, and hence fall short in making predictions that are both accurate and actionable. Most models lack transparency and cannot avoid privacy concerns due to the samples used from sensitive health data. This study specifically addresses the challenge of building a predictive healthcare framework capable of handling multimodal data, data streams that originate from different modalities (physiological, behavioral, and environmental), and exhibit intrinsic heterogeneity. Unlike general heterogeneous datasets, multimodal health data demands models that can effectively integrate structured, semistructured, and temporal information while ensuring privacy and interpretability. In this work, we propose an integrated comprehensive multimodal health prediction framework with five advanced methods, namely multimodal transformer networks (MTN), deep multitask neural networks (DMNN), Shapley additive explanations (SHAP) based explainability, Federated Learning, and Bayesian neural networks (BNN). MTN uses attention mechanisms to fuse different data modalities and captures cross-modal dependencies effectively, achieving an improvement of 8% to 12% in prediction accuracy. DMNN leverages multitask learning to share knowledge between related health prediction tasks, reducing error rates by 10%–15%. SHAP is used to provide localized, patient-specific explanations that improve clinical trust by up to 85%. This is done by privately training the models on decentralized datasets, which provides results without more than a 3% drop in precision compared with centralized models. Third, BNNs are used to quantify uncertainty in predictions, providing useful confidence intervals that improve clinical decision-making by 20%. The results obtained suggest significant improvements in predictive accuracy, transparency, and privacy preservation. This research not only improves health predictions through multimodal analysis but also tackles significant limitations in privacy, interpretability, and uncertainty quantification, thus promoting informed clinical decisions and customized patient care.