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.
Key words
COVID-19; Risk factors; Elderly people; Random Forest
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