Employing a random forest model to forecast the likelihood of coronary artery lesions in Kawasaki disease: a study centered on four biomarkers.

Background: Kawasaki disease is an acute immune vasculitis, which is more common in children under 5 years old. Kawasaki disease mainly affects the cardiovascular system, especially the coronary arteries. Once coronary artery damage occurs, it can significantly impact the patient’s prognosis. Therefore, in some countries and regions, Kawasaki disease has become a common acquired heart disease. Methods: First, univariate analysis was conducted on each predictive factor. Then, Least Absolute Shrinkage and Selection Operator and random forest algorithms were used to screen all predictive factors, and the prediction model was evaluated using receiver operating characteristic curve, calibration curve, and Decision Curve Analysis. Results: This study, based on data from 228 Kawasaki disease patients, utilized a random forest model to identify four predictive factors: white blood cell count, creatine kinase isoenzyme MB, albumin, and neutrophil count. These factors were used to construct a prediction model, which achieved an area under the curve of 0.743. Conclusions: We developed a forest plot based on white blood cell count, creatine kinase isoenzyme MB, albumin, and neutrophil count to effectively predict the occurrence of coronary artery lesions in Kawasaki disease. [ABSTRACT FROM AUTHOR]