Predicting COVID-19 Mortality and Identifying Clinical Symptom Patterns in Hospitalized Patients: A Machine-learning Study

Abstract

Background and Purpose: Identifying effective symptoms, demographic information, and underlying diseases to predict COVID-19 mortality is essential. We aimed to study the effective clinical and symptomatic characteristics of COVID-19 mortality in hospitalized patients with positive polymerase chain reaction (PCR) test results. Materials and Methods: For this study, we prospectively collected complete data on 26867 hospitalized individuals who tested PCR positive for COVID-19 from February 20, 2020, to September 12, 2021, in the Khorasan Razavi Province, Iran. We analyzed the data using artificial neural networks (ANN) and logistic regression (LR) models. Results: The accuracy of the ANN model was higher than the LR (90.27% versus 90.15%). The ten most important predictors that contributed to the prediction of death were decreasing consciousness level, cough, PO2 level, age, chronic kidney disease, fever, headache, smoking status, chronic blood diseases, and diarrhea using the ANN model. Conclusion: In conclusion, individuals suffering from chronic diseases such as cancer, kidney and blood diseases, as well as immunodeficiency are at a higher risk of mortality. This important finding can help decision-makers and medical professionals in their efforts to consider these conditions and provide effective preventative measures to reduce the risk of death.

Keywords

• machine learning
• sars-cov-2
• covid-19 diagnostic testing
• logistic regression
• neural network