Abstract
Background and objectives Group A Streptococcus (GAS) is the most frequent cause of bacterial pharyngitis, and it is advised to selectively use rapid antigen detection testing (RADT). Currently, the decision to perform this test is based on pediatricians' observations, but the criteria are not well-defined. Therefore, we utilized unsupervised learning to categorize patients based on the clinical manifestations of GAS pharyngitis. Our goal was to pinpoint the clinical symptoms that should prompt further examination and treatment in patients diagnosed with pharyngitis. Methods We analyzed categorical data from 305 RADT-positive patients aged three to 15 years using the K-modes clustering method. Each explanatory variable's relationship with cluster variables was statistically examined. Finally, we tested the differences between clusters for continuous variables statistically. Results The K-modes method categorized the cases into two clusters. Cluster 1 included older children with lymph node tenderness, while Cluster 2 consisted of younger children with cough and rhinorrhea. Conclusion Differentiating streptococcal pharyngitis from common cold or upper respiratory tract infection based on clinical symptoms alone is challenging, particularly in young patients. Future research should focus on identifying indicators that can aid in suspecting streptococcal infection in young patients.