Spatiotemporal prediction of obesity rates and model interpretability analysis from a public health perspective
Weiyan Tan, Bing Geng, XiuGuang Bai
TL;DR
This paper introduces a new Transformer model for predicting obesity rates, combining time and health data with spatial constraints to improve accuracy and provide interpretable insights for public health.
Contribution
The novel integration of temporal embeddings with spatially-constrained feature dependencies in a Transformer model for obesity prediction.
Findings
The proposed model outperforms existing models like LSTM and Mamba in predicting obesity rates across multiple states.
SHAP analysis reveals interpretable feature contributions, aiding in evidence-based public health resource allocation.
Abstract
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,…
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Taxonomy
TopicsMachine Learning in Healthcare · Chronic Disease Management Strategies · Time Series Analysis and Forecasting