Spatiotemporal prediction of obesity rates and model interpretability analysis from a public health perspective.

This study, focusing on the assessment of obesity prevalence trends in public health management, proposes an improved Transformer model that integrates temporal embeddings with spatially-constrained feature dependencies rather than purely geographic adjacency. Using state-level data from the CDC BRFSS, the method first performs joint temporal-health encoding (JTH) of obesity prevalence time series and health indicators. It then incorporates temporal decay and a learnable spatial constraint matrix (STA) into the attention mechanism, while employing dual-branch consistency training to enhance stability and generalization. We conducted comparative and ablation experiments on ten states, including Alaska and Alabama, and carried out independent validation on unseen states such as Guam and Idaho. The results show that the proposed approach outperforms representative models including MLP, LSTM, 1D-CNN, Mamba, iTransformer, and TimeMixer across metrics such as MAE, RMSE, sMAPE, R2, and MASE. Ablation experiments further demonstrate that JTH and STA contribute complementary improvements to model performance, while independent validation confirmed that the R2 values for all states exceeded 0.84. In addition, SHAP analysis was employed to illustrate the contributions and dependencies of key features, providing interpretable evidence to support, thereby guiding evidence-based resource allocation in obesity prevention and control.