Modeling analysis and reasonable prediction of medical costs are the basis and foundation for the determination of medical insurance costs. High-dimensional additional information in medical costs plays an important role in long-term prediction. This paper proposes a partial linear multi-indicator additive model to fit
and predict longitudinal medical cost data with high-dimensional features and uses two different dimensionality reduction estimation methods to estimate the model and applies the model to a set of high-dimensional dimensions. The longitudinal medical cost data of the variable is used for case analysis.