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

• Autor(a) principal: Lima,Tiago Pessoa Ferreira
• Data de Publicação: 2021
• Outros Autores: Sena,Gabrielle Ribeiro, Neves,Camila Soares, Vidal,Suely Arruda, Lima,Jurema Telles Oliveira, Mello,Maria Julia Gonçalves, Silva,Flávia Augusta de Orange Lins da Fonseca e
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Saúde Materno Infantil (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-38292021000300445
• topic: COVID-19; Risk factors; Elderly people; Random Forest

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.