Hemogram‐based decision tree models for discriminating COVID‐19 from RSV in infants

OBJECTIVE: Decision trees are efficient and reliable decision‐making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID‐19) and respiratory syncytial virus (RSV) infection in infants. METHODS: A cross‐sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS‐CoV‐2) infection and 44 infants with RSV infection. In total, 23 hemogram‐based instances were used to construct the decision tree models via 10‐fold cross‐validation method. RESULTS: The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one. CONCLUSION: Random forest and optimized forest models might have significant clinical applications, helping to speed up decision‐making when SARS‐CoV‐2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing.