Domínguez Olmedo, Juan LuisGragera Martínez, ÁlvaroMata Vázquez, JacintoPachón Álvarez, Victoria2023-02-142023-02-142022-10Domínguez-Olmedo, J. L., Gragera-Martínez, Á., Mata, J., & Pachón, V. (2022). Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models. In Healthcare (Vol. 10, Issue 10, p. 2027). MDPI AG. https://doi.org/10.3390/healthcare101020272227-9032 (electrónico)https://hdl.handle.net/10272/21567Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting modelsengAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/COVID-19Machine learningPredictionFeature importancejournal article10.3390/healthcare10102027open access33 Ciencias Tecnológicas