Machine-learning-algorithms-based diagnostic model for influenza A in children

Abstract

Background: At present, nucleic acid testing is the gold standard for diagnosing influenza A, however, this method is expensive, time-consuming, and unsuitable for promotion and use in grassroots hospitals. This study aimed to establish a diagnostic model that could accurately, quickly, and simply distinguish between influenza A and influenza like diseases. Methods: Patients with influenza-like symptoms were recruited between December 2019 and August 2023 at the Children’s Hospital Affiliated to Shandong University and basic information, nasopharyngeal swab and blood routine test data were included. Computer algorithms including random forest, GBDT, XGBoost and logistic regression (LR) were used to create the diagnostic model, and their performance was evaluated using the validation data sets. Results: A total of 4188 children with influenza-like symptoms were enrolled, of which 1992 were nucleic acid test positive and 2196 were matched negative. The diagnostic models based on the random forest, GBDT, XGBoost and logistic regression algorithms had AUC values of 0.835,0.872,0.867 and 0.784, respectively. The top 5 important features were lymphocyte (LYM) count, age, serum amyloid A (SAA), white blood cells (WBC) count and platelet-to-lymphocyte ratio (PLR). GBDT model had the best performance, the sensitivity and specificity were 77.23% and 80.29%, respectively. Conclusions: A computer algorithm diagnosis model of influenza A in children based on blood routine test data was established, which could identify children with influenza A more accurately in the early stage, and was easy to popularize.