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

作　　者：Chu-Xiong Gong Yue-Wen Li Ya-Min Li Zi-Yu Wang Hui-Qing Gao Xiao-Mei Liu 

机构地区：[1]Department of Infectious Diseases,Children’s Hospital Affiliated of Kunming Medical University,Kunming Children’s Hospital,Kunming 650228,China [2]Department of Rehabilitation and Palliative Medicine,The Third Affiliated Hospital of Kunming Medical University,Kunming 650000,China

出　　处：《Medical Data Mining》2024年第3期1-7,共7页TMR医学数据挖掘

基　　金：supported by the Joint Special Fund for Applied Basic Research of Yunnan Provincial Science and Technology Department and Kunming Medical University(202101AY070001-217).

摘　　要：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.

关 键 词：Kawasaki disease coronary artery lesions NOMOGRAM machine learning 

分 类 号：R54[医药卫生—心血管疾病]