16th International Conference on Software Engineering and Knowledge Engineering SEKE 2014, Hyatt Regency, Vancouver, CanadaArtificial neural networks for infectious diarrhea prediction using meteorological factors in ShanghaiYongming Wang, Junzhong Gu and Zili ZhouDepartment of Computer Science & Technology, East China Normal UniversityInstitute of Computer Applications, Shanghai, China
2OUTLINES Introduction Study area and dataset Prediction method and performance metricsDevelopment of FFBPNN modelinput and output parametersData pre-processing and post-processingDetermination of optimum network and parametersDevelopment of MLR modelExperiments results and discussionSensitivity analysesConclusions
3IntroductionAs a kind of common and important infectious disease, infectious diarrhea has a serious threat to human health and leads to one billion disease episodes and 1.8 million deaths each year (WHO, 2008).In Shanghai of China which is the biggest developing country, the incidence of infectious diarrhea has significant seasonality throughout the year and is particularly high in the summer and autumn of recent years.Hence, a robust short-term forecasting model for infectious diarrhea incidence is necessary for decision-making in policy and public health.
4IntroductionInfectious diseases have a closely relation with meteorological factors, such as temperature and rainfall, and can affect infectious diseases in a linear or nonlinear fashion. In recent years, there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health.As far as we are concerned with the prediction of diarrhea diseases in literature, many forecasting models based on statistical methods for diarrhea diseases forecasting have been reported.With regard to the fact that number of meteorological factor that effect infectious diarrhea are too much and the inter-relation among them is also very complicated, prediction models based on statistics methods may not be fully suitable for such type of problems.
5IntroductionNowadays, Artificial Neural Networks (ANNs) are considered to be one of the intelligent tools to understand the complex problems and have been widely used in the medical and health field. To the best knowledge of the authors, there is no works has been carried out to utilize the ANNs method in predicting diarrhea disease.Contribution: Establish a new ANNs model (FFBPNN) to predict infectious diarrhea in Shanghai with a set of meteorological factors as predictors.
6Study area and Dataset-Study area Shanghai is located in the eastern part of China which is the largest developing country in the world, and the city has a mild subtropical climate with four distinct seasons and abundant rainfalls. It is the most populous city in China comprising urban/suburban districts and counties, with a total area of 6,340.5 square kilometers and had a population of more then 25.0 million by the end of 2013.
7Study area and dataset-dataset The infectious diarrhea cases for the period
8Study area and dataset-dataset The meteorological factors data for the period
9Method and performance metrics Step 1: Data collectionStep 2: Data pre-processingStep 3: Data miningThe schematic flowchart of proposed method.
10Method and performance metrics Three layered feed-forward back-propagation artificial neural network model.
11Method and performance metrics The models with the smallest RMSE, MAE and MAPE and the largest R and R2 are considered to be the best models.
12Development FFBPNN model The FFBPNN modeling consists of two steps:--- Train the network using training dataset--- Model input and output parameters--- Data pre-processing and post-processing--- Determination of optimum network and parameters--- Test the network with testing datasetHidden neurons and network errors
13Development FFBPNN model ParametersFFBPNNNumber of input layer units9Number of hidden layer1Number of hidden layer units4Number of output layer unitsMomentum rate0.9Learning rate0.74Error after learning1e-6Learning cycle1500 epochTransfer function in hidden layerTansigTransfer function in output layerPurelinTraining functionTRAINGDMThe optimum model architecture and parameters for the diarrhea prediction.
14Development MLR model Dependent variable : diarrhea number Independent variables : meteorological factors
15Results and discussion PECsModelsFFBPNNMLRTrainingTestingMAERMSEMAPE(%)27.27%38.41%43.37%41.82%R0.87830.84900.80890.6968R20.92130.91250.88110.8388The reason of better performances of the FFBPNN model over MLR model may be attributed to the complex nonlinear relationship between infectious diseases and meteorological factors.
16Results and discussion MLRFFBPNNComparison curves plot of actual vs. predicted trends for training dataset
17Results and discussion MLRFFBPNNComparison scatter plot of actual vs. predicted values for training dataset
18Results and discussion MLRFFBPNNComparison curves plot of actual vs. predicted trends for testing dataset
19Results and discussion MLRFFBPNNComparison scatter plot of actual vs. predicted values for testing dataset
22Conclusions1. The proposed method is more suitable for prediction infectious diarrhea then statistical methods MLR.2. The feed-forward back-propagation neural network (FFBPNN) model with architecture has the best accurate prediction results in prediction of the weekly number of infectious diarrhea.3. most effective meteorological factor on the infectious diarrhea is weekly average temperature, whereas weekly average rainfall is the least effective parameter on the infectious diarrhea.Therefore, this technique can be used to predict infectious diarrhea. The results can be used as a baseline against which to compare other prediction techniques in the future.