Domínguez-Gutiérrez, UlisesUlisesDomínguez-GutiérrezRodríguez Aguilar, RománRománRodríguez Aguilar2024-03-012024-03-012024-01-01Domínguez-Gutiérrez, U., Rodríguez-Aguilar, R. (2024). Catastrophic Health Spending by COVID-19 in the Mexican Insurance Sector. In: Marmolejo-Saucedo, J.A., Rodríguez-Aguilar, R., Vasant, P., Litvinchev, I., Retana-Blanco, B.M. (eds) Computer Science and Engineering in Health Services. COMPSE 2022. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-34750-4_149783031347498https://scripta.up.edu.mx/handle/20.500.12552/997810.1007/978-3-031-34750-4_142-s2.0-85174492192The COVID-19 pandemic that the world has been suffering for 3 years has generated major impacts worldwide, both in public health systems and in the private insurance industry. The high costs of care derived from cases with complications have likewise generated a great impact on the private insurance industry. In the case of Mexico, the mortality rates observed are among the first places, in addition to generating a great impact on private insurance. This work deals with the measurement of the impact of catastrophic expenses derived from COVID-19 in an insurance company; using a set of machine learning models, the key variables in the estimation of patients with potential catastrophic expenses were determined. The results show that the estimated classification model has a positive performance in addition to allowing the identification of the main risk factors of the insured as well as their potentially catastrophic impact on insurance companies.© 2024 Springer NatureenMachine learningCOVID-19Private insuranceCatastrophic health spendingMéxicoResource Types::text::book::book part