Predicting medical insurance costs using an arithmetic multi-attention transformer

Predicting medical insurance costs from increasingly complex healthcare data requires models that can capture nonlinear relationships among demographic, behavioural, and health-related factors. Existing attention-based approaches for tabular data typically model either additive or multiplicative feature interactions independently, limiting their ability to capture complementary patterns. We constructed a computational model to predict medical insurance costs based on Arithmetic Multi-attention Transformer (AMFormer), a deep learning architecture that introduces a dual-stream arithmetic attention mechanism jointly modeling additive and multiplicative interactions within a unified framework. This design enables the model to capture both linear feature contributions and their complex multiplicative dependencies through logarithmic-space transformations, distinguishing it from prior approaches that treat these interactions separately. We evaluate our model on a benchmark medical insurance dataset, comparing it against representative traditional machine learning methods and state-of-the-art deep learning baselines. Our model achieves better performance compared to other methods. Ablation analyses demonstrate that the synergistic combination of additive and multiplicative attention streams with logarithmic transformation is critical for these performance gains.