Detection and severity of COVID-19 cases based on patient symptoms Using decision trees

Benjamín Luna Benoso (), José Cruz Martínez Perales (), Úrsula Samantha Morales Rodríguez () and Jorge Cortés Galicia ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 2, 448-456

Abstract: At the end of 2019, a type of pneumonia of unknown origin was detected in Wuhan, China. It was later determined that the illness was caused by the SARS-CoV-2 virus, and in 2020, the World Health Organization designated this disease as COVID-19. Various efforts have been made to enable timely detection of COVID-19 within the field of computational systems. This study proposes detecting COVID-19 based on the symptoms experienced by patients, utilizing data provided by the National Epidemiological Surveillance System (SINAVE) of Mexico City, from which 403,185 records were used. In situations where positive cases of COVID-19 are detected, it is predicted whether it will be a serious case in which the patient needs to be intubated or admitted to the Intensive Care Unit. For this, classification and regression decision trees (CART) are used. Different parameters were considered to define the CART model, and the stepwise variable selection process was also used to determine the significant variables that offer the best results, obtaining an accuracy of 87.04%. This study shows progress in the detection of COVID-19 using only the symptoms presented by the patients.

Keywords: COVID-19; Decision trees; Machine learning; Variable selection. (search for similar items in EconPapers)
Date: 2025
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