Objective To establish a Kawasaki disease mathematical diagnosis model in order to sup-port clinical decision-making. Methods Children with fever admitted to Shanghai Children's Hospital from Jan-uary 2013 to July 2017 were recruited and were divided into Kawasaki disease group and other disease control groups according to the final clinical diagnosis. The general clinical information and laboratory indicators were compared,a mathematical model was established and evaluated through the logistic regression analysis. Results A total of 1916 children were enrolled in this study,with an average age of 3. 47 ± 2. 83 years. Of these,1085 (56. 6％) were male,831 (43. 4％) were female,479 (25. 0％) were diagnosed with Kawasaki disease and 1099 (75. 0％) were with other diagnosis. Logistic regression analysis included dependent variables and inde-pendent variables,and the results showed that the Hosmer and Lemeshow test of this model was P=0. 944,the difference was not significant,indicating that the fitting equation and the true equation without deviation; age , fever days,ESR,CRP,WBC,ALB and DD dimers were independent risk factors for Kawasaki disease. The pre-dictive equation of Logistic regression is:ln P1-p( )= -7. 337 +2. 163 × CRP+1. 56 × DD+1. 612 × ESR+1. 392+age+1. 724 × days of fever +2. 295 × WBC +0. 808 × ALB. The patient model score and the ROC curve was calculated. The area under the curve was 0. 927 (95％ CI:0. 905-0. 950). When the score was 9,the Youden index was the highest(72. 9％),the sensitivity and specificity were 89. 7％ and 83. 2％. Conclusion The Kawasaki disease diagnosis mathematical model established in this study has good diagnostic efficacy,which need to be confirmed by further large-scale,multicenter studies.