基于BP神经网络预测儿童甲状腺疾病的模型研究

Prediction model of pediatric thyroid disease based on back-propagation neural network

目的:构建儿童甲状腺疾病的预测模型。方法:根据某市疾病预防控制中心2013年~2016年采集的1400名8~11岁儿童的体检数据及临床初步诊断结果作为研究数据,随机抽取其中的1000名儿童作为训练样本,剩余的400名儿童作为测试样本,利用MATLAB R2018b软件编程实现三层BP神经网络模型。结果:当选择log&log组合作为隐含层和输出层的传递函数,隐含层节点数目选择8时,模型的分类正确率达到91.43%。结论:BP神经网络应用于儿童甲状腺疾病的预测,可以为疾病的防治工作提供理论依据。

Objective To construct a prediction model of thyroid disease in pediatric patients.Methods The physical examination data and preliminary clinical diagnoses of 1400 children aged 8-11 years were collected from 2013 to 2016 in a Center for Disease Prevention and Control.One thousand out of 1400 children were randomly selected as training samples,and the remaining 400 were used as test samples.A three-layer back-propagation neural network model was constructed by MATLAB R2018b software.Results The classification accuracy of the model reached 91.43%when the combination of log&log was selected as the transfer function of the hidden layer and the output layer,with 8 nodes in the hidden layer.Conclusion Back-propagation neural network can be applied to the prediction of thyroid diseases in children,and provide a theoretical basis for the prevention and treatment of diseases.