Application of extreme learning machine model in prediction of hand-foot-and-mouth disease incidence in Zhangjiakou city
10.16781/j.0258-879x.2018.02.0226
- Author:
Xu YANG
1
Author Information
1. Center for Disease Control and Prevention of Zhangjiakou
- Publication Type:Journal Article
- Keywords:
Extreme learning machine;
Hand-foot-and-mouth disease;
Morbidity;
Neural network;
Prediction
- From:
Academic Journal of Second Military Medical University
2018;39(2):226-230
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the application of extreme learning machine (ELM) model in predicting the incidence of hand-foot-and-mouth disease, and to compare the difference between ELM model and neural network model. Methods The monthly incidence data of hand-foot-and-mouth disease from May 2008 to Jul. 2017 in Zhangjiakou were collected and formed a time series with 111 data. To validate and evaluate the prediction performance of the two models, 75% of the randomly selected dataset were used to train model and the remaining 25% were used as testing data for prediction. Results and conclusion The mean relative errors (MREs) of learning and prediction based on ELM model were 0.05 and 0.07, respectively. The MREs of learning and prediction based on neural network model were 0.09 and 0.12, respectively. The learning and prediction effects of ELM model are better than neural network model. It can improve the accuracy of prediction and has high application value.
