A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data

Shirmohammadi‐Khorram, Nasrin; Tapak, Leili; Hamidi, Omid; Maryanaji, Zohreh

doi:10.1111/zph.12622

Cited by 15 publications

(16 citation statements)

References 31 publications

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“…At the same time, a model that can better fit the nonlinear data is considered to yield the expected effect. A lot of machine learning methods such as artificial neural network, 27 38 43 support vector machine 44 and random forest 45 were widely used in brucellosis prediction and achieved good forecasting performance. The XGBoost model, a relatively new model, was first proposed by Chen Tianqi and Carlos Gestrin in 2011.…”

Section: Discussionmentioning

confidence: 99%

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Alim

Guan

et al. 2020

BMJ Open

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ObjectivesHuman brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China.DesignTime-series study.SettingMainland China.MethodsData on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction.ResultsThe number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively.ConclusionsThe performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

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Section: Discussionmentioning

confidence: 99%

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Alim

Guan

et al. 2020

BMJ Open

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“…Model assessment criteria. To assess and compare the accuracy of prediction and the performance of the models in the times series data modeling in this study, the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Root Error (MARE), and R 2 determination coefficient criteria were used, which were calculated by the following relations [28,44]:…”

Section: Plos Onementioning

confidence: 99%

“…Two pioneer methods in neural networks are the Radial Basis Function (RBF) and the Multilayer Perceptron (MLP) networks. RBF is a more common type of neural network learning which responds to a limited section of the input space; it has a faster and more accurate and yet simpler network structure compared to other neural networks, while the MLP is more generalizable [28]. Another machine learning method is the Support Vector Machine (SVM) method.…”

Section: Introductionmentioning

confidence: 99%

“…SVM success in forecasting time series of different fields of science led us to the conclusion that we should use it for forecasting brucellosis time series. Many researchers have approved the desirable performance of these four techniques and their advantages in forecast [28]. Nonetheless, in spite of the widespread application of these techniques, they have not-to our knowledge-been evaluated for Qazvin's brucellosis data.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019

et al. 2020

Self Cite

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Background The identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system. Thus, this study was conducted to assess and compare the performance of four machine-learning methods in modeling and forecasting brucellosis time series data based on climatic parameters. Methods In this cohort study, human brucellosis cases and climatic parameters were analyzed on a monthly basis for the Qazvin province-located in northwestern Iran-over a period of 9 years (2010-2018). The data were classified into two subsets of education (80%) and testing (20%). Artificial neural network methods (radial basis function and multilayer perceptron), support vector machine and random forest were fitted to each set. Performance analysis of the models were done using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Root Error (MARE), and R 2 criteria. Results The incidence rate of the brucellosis in Qazvin province was 27.43 per 100,000 during 2010-2019. Based on our results, the values of the RMSE (0.22), MAE (0.175), MARE (0.007) criteria were smaller for the multilayer perceptron neural network than their values in the other three models. Moreover, the R 2 (0.99) value was bigger in this model. Therefore, the multilayer perceptron neural network exhibited better performance in forecasting the studied data. The average wind speed and mean temperature were the most effective climatic parameters in the incidence of this disease.

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“…To discover the daily POI exploitation pattern from an LTE access pattern, we leverage a random forest regression (RFR) model which can be used for time-series pattern prediction [20,21]. Figure 4 illustrates our RFR model and its training process.…”

Section: Daily Poi Exploitation Pattern Discoverymentioning

confidence: 99%

Discovering Daily POI Exploitation Using LTE Cell Tower Access Traces in Urban Environment

Han

Park

Lee

2020

Lecture Notes in Computer Science

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Point of interest (POI) in an urban space represents the perception of city dwellers and visitors of a certain place. LTE cell tower access trace data is one of the promising data sources which has the potential to show real-time POI exploitation analysis. However, there is not much discussion on how it is correlated to diachronic POIs and their exploitation pattern. In this paper, we first show that the access trace pattern from the LTE cell tower can be used to discover which types of POIs exist in a certain area. Then, we propose a daily POI exploitation discovery scheme which can extract patterns of how POIs are daily used. Our analysis can provide a good insight into future urban space-based services such as urban planning and tourism.

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A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data

Cited by 15 publications

References 31 publications

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study

Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019

Discovering Daily POI Exploitation Using LTE Cell Tower Access Traces in Urban Environment

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