Work

Machine learning for COVID-19 mortality prediction: enhancing cart models with node-specific odds ratios

Year: 2026

Type: article

Abstract: Objective: The objective of the study is to evaluate a classification and regression tree (CART) model combined with odds ratio (OR) to identify key predictors of COVID-19 mortality.Data from 1,432 patients hospitalized at Hospital General de México during the pandemic's 1 st year were analyzed.Methods: A CART model was constructed using demographic, clinical, and laboratory data collected at admission.The model's performance was evaluated using multiple criteria, and ORs were calculated for each node to measure mortality.Results: Mechanical ventilation emerged as the strongest predictor of mortality.Patients intubated at admission with hospital stays under 17.5 days had the highest mortality rate (99%, OR = 80).Conversely, nonintubated patients hospitalized over 5.5 days with glomerular filtration rates above 66.5 had the lowest mortality (5%, OR = 0.26).The model showed excellent performance, with an F1 score of 0.918, accuracy of 0.903, and area under the curve of 0.955.Conclusions: The CART model, combined with OR calculations, offers a reliable and comprehensible tool for predicting COVID-19 mortality risk.This model provides practical utility in various healthcare settings, including resource-limited contexts, by focusing on readily available clinical parameters.The results emphasize the significant influence of mechanical ventilation on patient outcomes and the need for timely interventions in high-risk patients.

Source: Cirugía y Cirujanos (English Edition)

DOI: 10.24875/cirue.m25000948