The Predictive Model for Coronary Artery Lesions in Kawasaki Disease Based on Neural Network
Zhang Sheng1, Tian Jie2, Fan Chu1, Tan Xuhai2, Li Zhe1, He Xiangqian1*
1 College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China 2 College of Pediatrics, Chongqing Medical University, Chongqing 400016, China
Abstract：The objective of the study is to find out the risk factors for coronary artery lesions (CAL) in Kawasaki disease (KD) and build the predictive model. The electronic medical record (EMR) data of 1000 KD patients (343 KD with CAL) was collected including the demographic data, laboratory test data, echocardiography and diagnosis data, which were pre-processed for analysis. The risk factors for CAL in KD were selected using association rules. The data set was divided into training set (70%) and testing set (30%), and the neural network (NN) model and logistic regression (LR) model were built. The predictive performance of the two models was evaluated. Results showed that the sensitivity, specificity, accuracy and AUC (Area Under the ROC Curve) of NN model was 0.718, 0.746, 0.737 and 0.796 respectively, which was better than those obtained from LR model[0.175, 0.893, 0.647 and 0.624 respectively]. Thus, the performance of NN model to predict CAL in KD is better than that of LR model.
张胜,田杰,樊楚,谭续海,李哲,贺向前. 基于神经网络的川崎病并发冠状动脉病变预测模型[J]. 中国生物医学工程学报, 2018, 37(3): 313-318.
Zhang Sheng, Tian Jie, Fan Chu, Tan Xuhai, Li Zhe, He Xiangqian. The Predictive Model for Coronary Artery Lesions in Kawasaki Disease Based on Neural Network. Chinese Journal of Biomedical Engineering, 2018, 37(3): 313-318.
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