Vanegas-Lopez, JJVanegas-LopezVasquez, FabianFabianVasquez2025-04-232025-04-23201710.1016/j.gaceta.2016.10.003https://sic.vriic.usach.cl/entities/publication/b82198a2-8f78-4d19-b61d-bad429307738Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models: selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978–2008. © 2016 SESPASesForecastingMethodsNon-parametric statisticshttps://doi.org/10.1016/j.gaceta.2016.10.003