Deep learning time series prediction models in surveillance data of hepatitis incidence in China

Abstract

Background Precise incidence prediction of Hepatitis infectious disease is critical for early prevention and better government strategic planning. In this paper, we presented different prediction models using deep learning methods based on the monthly incidence of Hepatitis through a national public health surveillance system in China mainland. Methods We assessed and compared the performance of three deep learning methods, namely, Long Short-Term Memory (LSTM) prediction model, Recurrent Neural Network (RNN) prediction model, and Back Propagation Neural Network (BPNN) prediction model. The data collected from 2005 to 2018 were used for the training and prediction model, while the data are split via 5-Fold cross-validation. The performance was evaluated based on three metrics: mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results Among the year 2005–2018, 20,924,951 cases and 11,892 deaths were supervised in the system. Hepatitis B (HB) is the most disease-causing incidence and death, and the proportion is greater than 70 percent, while the percentage of the incidence and deaths is decreased much in 2018 compared with 2005. Based on the measured errors and the visualization of the three neural networks, there is no one model predicting the incidence cases that can be completely superior to other models. When predicting the number of incidence cases for HB, the performance ranking of the three models from high to low is LSTM, BPNN, RNN, while it is LSTM, RNN, BPNN for Hepatitis C (HC). while the MAE, MSE and MAPE of the LSTM model for HB, HC are 3.84*10−06, 3.08*10−11, 4.981, 8.84*10−06, 1.98*10−12,5.8519, respectively. Conclusions The deep learning time series predictive models show their significance to forecast the Hepatitis incidence and have the potential to assist the decision-makers in making efficient decisions for the early detection of the disease incidents, which would significantly promote Hepatitis disease control and management.

Top-Down Proteomics Detection of Potential Salivary Biomarkers for Autoimmune Liver Diseases Classification

“…The investigated proteins/peptides belong to the following families: acidic proline-rich proteins (aPRPs); statherin and P-B peptide; histatins (Hst); salivary cystatins (S-type); cystatins A, B, C, and D; α-defensins; antileukoproteinase (SLPI); S100A7, S100A8, S100A9, S100A12 proteins. Several variants and post-translationally modified proteoforms, previously characterized in human saliva by our proteomic approach [ 39 ], were also investigated. The post-translation modifications (PTMs) considered were phosphorylation, proteolysis, N-terminal acetylation, methionine or tryptophan oxidation, and cysteine S-modification (glutathionylation, cysteinylation, nitrosylation and formation of dimers by disulphide bridges).…”

“…In recent years, substantial clinical breakthroughs using Machine Learning (ML) applications have been made, including disease prevention, diagnosis, prognosis, drug discovery and clinical trial design [ 35 , 36 ]. There are multiple examples in the literature where predictive ML models have been used to identify diagnostic biomarkers in immune mediated inflammatory diseases [ 35 , 37 ], liver diseases comprised [ 38 , 39 , 40 ].…”

Development and comparison of predictive models for sexually transmitted diseases—AIDS, gonorrhea, and syphilis in China, 2011–2021

“…For hybrid models, the hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA ( p, d, q ) ( P, D, Q ) s model and the basic generalized regression neural network (GRNN) model ( 17 ). LSTM model has demonstrated better performance than BPNN in forecasting hepatitis incidence in China ( 42 ), and better than the recurrent neural network in forecasting COVID-19 in Malaysia, Morocco and Saudi Arabia ( 43 ). The different findings of these studies suggest that further studies comparing different kinds of forecasting methods for different kinds of diseases are necessary for the application in predicting epidemic behavior.…”

A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang

“…Particularly, RNN is a sub-class of artificial neural network using hidden variables as a memory to capture temporal dependencies between system and control variables, which is more suitable for handling time series data [ 28 ]. So it is widely used to predict the incidence of various diseases, such as hepatitis [ 29 ], hands-foot-and-mouth disease [ 30 ], COVID-19 [ 31 , 32 ], dengue fever [ 33 ]. For example, Xia et al .…”