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Healthcare cost prediction is a challenging task due to thehigh-dimensionality and high correlation among covariates. Additionally, theskewed, heavy-tailed, and often multi-modal nature of cost data can complicatematters further due to unobserved heterogeneity. In this study, we propose anovel framework for finite mixture regression models that incorporatescovariate clustering methods to better account for the effects of clusteredcovariates on subgroups of the outcome, which enables a more accuratecharacterization of the complex distribution of the data. The proposedframework can be formulated as a convex optimization problem with an additionalpenalty term based on the prior similarity of the covariates. To efficientlysolve this optimization problem, a specialized EM-ADMM algorithm is proposedthat integrates the alternating direction multiplicative method (ADMM) into theiterative process of the expectation-maximizing (EM) algorithm. The convergenceof the algorithm and the efficiency of the covariate clustering method areverified using simulation data, and the superiority of the approach overtraditional regression techniques is demonstrated using two real Chinesemedical expenditure datasets. Our empirical results provide valuable insightsinto the complex network graph of the covariates and can inform businesspractices, such as the design and pricing of medical insurance products.

Analyzing covariate clustering effects in healthcare cost subgroups: insights and applications for prediction