Abstract
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.
Highlights
From the beginning of the COVID-19 pandemic until September 27, 2020, Brazil, the largest country in South America and the fifth largest in the world, was already considered the second country in number of deaths from the disease
While the overall lethality rate in Pernambuco at the end of the first three months of the pandemic was 8.25%,12 the lethality rate for elderly patients in the same period was 41.81%.This value was much higher than the rates found in the literature, which ranged from 5.6% to 28.6%
Several articles show that the presence of comorbidities is a risk factor for adverse clinical outcomes such as death,[16,17,18,19,20,21] with cardiovascular disease always being one of the most prevalent comorbidities in the samples analyzed
Summary
From the beginning of the COVID-19 pandemic (coronavirus 2019 disease) until September 27, 2020, Brazil, the largest country in South America and the fifth largest in the world, was already considered the second country in number of deaths from the disease. Data extracted from patients with COVID-19 are a valuable source of information about both the pathophysiology of the disease and the risk factors associated with death. These data have been widely studied, and it is currently agreed that advanced age and the presence of comorbidities are associated with increased morbidity and mortality.[2] The abundant availability of these data allows the construction of the Learning Machine (LM) algorithms - a branch of Artificial Intelligence - in which it is possible to identify more susceptible people based on individual features. Through methods called Classification, the algorithm learns during a process called training by receiving a set of inputs (clinical characteristics) along with the outputs (outcome). The algorithm is able to predict an output from inputs not seen during training
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