梅毒仍然是中国主要的公共卫生问题。目的构建梅毒流行趋势预测的优化模型,为预防和控制梅毒提供有效的预防措施。
■1982年至2020年的梅毒发病率数据来自《中国卫生统计年鉴》。建立了指数平滑模型(ES模型)和BP神经网络模型,在此基础上,建立ES-BP组合模型。评估预测性能以比较MAE(平均绝对误差),MSE(均方误差),MAPE(平均绝对百分比误差),和RMSE(均方根误差)。
■最佳ES模型是布朗的线性趋势模型,MAE和MAPE值最低,其残差为白噪声序列(P=0.359)。最优BP神经网络模型有三层,输入节点数,隐藏,和输出层设置为5、11和1,以及MAE的平均值,MSE,通过五倍交叉验证,RMSE分别为1.519、6.894和1.969。ES-BP组合模型有三层,与模型节点1、4和1。MAE的最低平均值,MSE,通过五折交叉验证获得的RMSE分别为1.265、5.739和2.105。
■ES,BP神经网络,ES-BP组合模型可用于预测梅毒发病率,但是ES-BP组合模型的预测性能优于基本ES模型和基本BP神经网络模型。
UNASSIGNED: Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control.
UNASSIGNED: Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error).
UNASSIGNED: The optimum ES model was Brown\'s linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively.
UNASSIGNED: The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.