Death risk and the importance of clinical features in elderly people with covid-19 using the random forest algorithm

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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.

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Lima, T. P. F., Sena, G. R., Neves, C. S., Vidal, S. A., Lima, J. T. O., Mello, M. J. G., & Silva, F. A. de O. L. da F. e. (2021). Death risk and the importance of clinical features in elderly people with covid-19 using the random forest algorithm. Revista Brasileira de Saude Materno Infantil, 21, S445–S451. https://doi.org/10.1590/1806-9304202100S200007

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