Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions

Nicola Caravaggio () and Giuliano Resce ()

Economics & Statistics Discussion Papers from University of Molise, Department of Economics

Abstract: Accurate forecasting of healthcare costs is essential for making decisions, shaping policies, preparing finances, and managing resources effectively, but traditional econometric models fall short in addressing this policy challenge adequately. This paper introduces machine learning to predict healthcare expenditure in systems with heterogeneous regional needs. The Italian NHS is used as a case study, with administrative data spanning the years 1994 to 2019. The empirical analysis utilises four machine learning algorithms (Elastic-Net, Gradient Boosting, Random Forest, and Support Vector Regression) and a multivariate regression as a baseline. Gradient Boosting emerges as the superior algorithm in out-of-the-sample prediction performances; even when applied to 2019 data, the models trained up to 2018 demonstrate robust forecasting abilities. Important predictors of expenditure include temporal factors, average family size, regional area, GDP per capita, and life expectancy. The remarkable effectiveness of the model demonstrates that machine learning can be efficiently employed to distribute national healthcare funds to areas with heterogeneous needs.

Keywords: Machine Learning; National Health System; Healthcare expenditure (search for similar items in EconPapers)
JEL-codes: C54 H51 I10 (search for similar items in EconPapers)
Pages: 45
Date: 2023-10-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ger
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