BP neural network model for the differentiation of Kawasaki disease and febrile illnesses based on data mining
Objective A BP neural network model for diagnosing Kawasaki disease(KD) based on laboratory tests and clinical symptoms was developed and evaluated.Methods Consecutive cases of diagnosis for KD and other common febrile illnesses in electronic medical record system of Children's Hospital of Chongqing Medical University from January 2007 to January 2016 was collected as the study subject.Subjects were randomized into training cohort and test cohort using random sampling function in R 3.2.3.Totally 51 clinical information including demographic data,laboratory tests and clinical symptoms were collected and analyzed by univariate analysis to identify significant variables.The diagnostic model was established using Logistic regression analysis and BP neural network,respectively.And the diagnostic performance of the two methods was compared.Results A total of 905 patients with KD and 438 patients with other febrile illnesses were included:1 042 patients (700 patients with KD,342 patients with other febrile illnesses) as the training cohort and 301 patients (205 patients with KD,96 patients with other febrile illnesses) as the testing cohort.Univariate analysis showed that 37 variables had significant difference between KD and other febrile illness.Logistic regression analysis showed that 16 variables were included in the optimal regression equation.This BP neural network had 37 input layer nodes,24 hidden layer nodes and 1 output layer nodes.Logistic regression analysis accurately diagnosed 84.1％ of training cohort and 82.1％ of testing cohort,the ROC analysis of Logistic regression revealed that AUC was 0.91 in training cohort and 0.89 in testing cohort.The accuracy of BP neural network was 96.4％ and 86％,AUC was 0.94 and 0.92.These two models showed reasonably high sensitivity.The specificity of BP neural network model was significantly higher than that of Logistic regression model.Conclusion A BP neural network model was developed,which has important accessory diagnostic value for diagnosis of KD.But all these conclusions need further validation in clinic.
Kawasaki diseaseFebrile illnessesLogistic regressionBP neural networkDiagnostic model
国家科技期刊平台
NSTL
万方数据
维普
