Death risk and the importance of clinical features in elderly people with COVID-19 using the Random Forest Algorithm

• Main Authors: Tiago Pessoa Ferreira Lima (Author), Gabrielle Ribeiro Sena (Author), Camila Soares Neves (Author), Suely Arruda Vidal (Author), Jurema Telles Oliveira Lima (Author), Maria Julia Gonçalves Mello (Author), Flávia Augusta de Orange Lins da Fonseca e Silva (Author)
• Format: Book
• Published: Instituto Materno Infantil de Pernambuco.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Abstract Objectives: train a Random Forest (RF) classifier to estimate death risk in elderly people (over 60 years old) diagnosed with COVID-19 in Pernambuco. A "feature" of this classifier, called feature importance, was used to identify the attributes (main risk factors) related to the outcome (cure or death) through gaining information. Methods: data from confirmed cases of COVID-19 was obtained between February 13 and June 19, 2020, in Pernambuco, Brazil. The K-fold Cross Validation algorithm (K=10) assessed RF performance and the importance of clinical features. Results: the RF algorithm correctly classified 78.33% of the elderly people, with AUC of 0.839. Advanced age was the factor representing the highest risk of death. The main comorbidity and symptom were cardiovascular disease and oxygen saturation ≤ 95%, respectively. Conclusion: this study applied the RF classifier to predict risk of death and identified the main clinical features related to this outcome in elderly people with COVID-19 in the state of Pernambuco.

Item Description

1806-9304
10.1590/1806-9304202100s200007