Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China

Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

doi:10.1155/2015/328273

Cited by 21 publications

(15 citation statements)

References 20 publications

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“…Ture compared time series prediction capabilities of three artificial neural networks (ANN), algorithms (multi-layer perceptron (MLP), radial basis function (RBF), time delay neural networks (TDNN)), and an ARIMA model to hepatitis A virus (HAV) forecasting (Ture & Kurt, 2006). Gan used a hybrid algorithm combining grey model and back propagation artificial neural network to forecast hepatitis B in China (Gan et al, 2015). A mathematical model of HBV transmission was used to predict future chronic hepatitis B (CHB) prevalence in the New Zealand Tongan population with different infection control strategies in literature (Thornley, Bullen & Roberts, 2008).…”

Section: Introductionmentioning

confidence: 99%

Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models

Gan¹,

Chen²,

Huang³

2016

PeerJ

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This study compares and evaluates the prediction of hepatitis in Guangxi Province, China by using back propagation neural networks based genetic algorithm (BPNN-GA), generalized regression neural networks (GRNN), and wavelet neural networks (WNN). In order to compare the results of forecasting, the data obtained from 2004 to 2013 and 2014 were used as modeling and forecasting samples, respectively. The results show that when the small data set of hepatitis has seasonal fluctuation, the prediction result by BPNN-GA will be better than the two other methods. The WNN method is suitable for predicting the large data set of hepatitis that has seasonal fluctuation and the same for the GRNN method when the data increases steadily.

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Section: Introductionmentioning

confidence: 99%

Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models

Gan¹,

Chen²,

Huang³

2016

PeerJ

Self Cite

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“…Grey prediction is an estimation of a grey system. Grey predication makes scientific, quantitative forecasts about the future output of a system by generating and extracting the useful information from a small number of samples and partially known information, which has a good application in the engineering field [37, 38]. The single variable first order grey model, which is abbreviated as GM(1,1), is the main and basic model of grey prediction.…”

Section: Methodsmentioning

confidence: 99%

Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography

et al. 2017

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BackgroundElectrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data.MethodsWeighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes. Disconnected electrodes were identified by filtering out abnormal coefficients with discrete wavelet transforms. Further, previously valid measurements were utilized to establish grey model. The invalid frames after electrode disconnection were substituted with the data estimated by grey model. The proposed approach was evaluated on resistor phantom and with eight patients in clinical settings.ResultsThe proposed method was able to detect 1 or 2 disconnected electrodes with an accuracy of 100%; to detect 3 and 4 disconnected electrodes with accuracy of 92 and 84% respectively. The time cost of electrode detection was within 0.018 s. Further, the proposed method was capable to compensate at least 60 subsequent frames of data and restore the normal image reconstruction within 0.4 s and with a mean relative error smaller than 0.01%.ConclusionsIn this paper, we proposed a two-step approach to detect multiple disconnected electrodes and to compensate the invalid frames of data after disconnection. Our method is capable of detecting more disconnected electrodes with higher accuracy compared to methods proposed in previous studies. Further, our method provides estimations during the faulty measurement period until the medical staff reconnects the electrodes. This work would improve the clinical practicability of dynamic brain EIT and contribute to its further promotion.

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“…Similarly, Belciug and Gorunescu 76 implemented genetic algorithm weight training upon multi-layer perceptron neural network to assess the detection and recurrence of breast cancer. Gan et al 77 combined the grey model (GM) and back propagation (BP) neural network to predict the growth of hepatitis B, where the results proved that the hybrid has an advantage over GM (1,1) and GM (2,1) in performance evaluations.…”

Section: Hybrid Ann For Epidemic Forecastmentioning

confidence: 99%

A Review of Epidemic Forecasting Using Artificial Neural Networks

Philemon

Ismail

Jayeola

2019

Int J Epidemiol Res

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Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.

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Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China

Cited by 21 publications

References 20 publications

Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models

Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models

Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography

A Review of Epidemic Forecasting Using Artificial Neural Networks

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