A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

Yin, Yi; Xue, Mingyue; Shi, Lingen; Qiu, Tao; Xia, Derun; Fu, Gengfeng; Peng, Zhihang

doi:10.1155/2021/6696041

Cited by 5 publications

(7 citation statements)

References 41 publications

(47 reference statements)

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“…[17] However, Decision tree, Random forest, AdaBoost, XGBoost, and GRNN prediction techniques, described as machine-learning-based forecasting models, specialize in dealing with big data and nonlinear problems but require a large number of samples. [32] For example, Yin et al [32] used Decision tree, Random forest, AdaBoost, XGBoost, and GRNN prediction techniques to fit and predict patients co-infected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) in Jiangsu, China, which provided 12469 samples sizes in the study. Compared to GM (1, 1) and machine-learning-based forecasting models, the SARIMA model has numerous advantages for infectious disease prediction.…”

Section: Discussionmentioning

confidence: 99%

“…It should be noted that according to the data features of the study, choosing the right model is a prerequisite for exploring credible results. Based on a literature review and our previous research, we found that the ARIMA, [3] GM (1, 1) model, [3] Decision tree, [32] Random forest, [32] AdaBoost with decision tree (AdaBoost), [32] extreme gradient boosting decision tree (XGBoost), [32] Elman network, [33] and generalized regression neural network (GRNN) [34] can be used to predict hepatitis B. Among these, the GM (1,1) model is suitable for short-term prediction with small sample sizes [17] .…”

Section: Discussionmentioning

confidence: 99%

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The research of SARIMA model for prediction of hepatitis B in mainland China

et al. 2022

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Hepatitis B virus infection is a major global public health concern. This study explored the epidemic characteristics and tendency of hepatitis B in 31 provinces of mainland China, constructed a SARIMA model for prediction, and provided corresponding preventive measures. Monthly hepatitis B case data from mainland China from 2013 to 2020 were obtained from the website of the National Health Commission of the People's Republic of China. Monthly data from 2013 to 2020 were used to build the SARIMA model and data from 2021 were used to test the model. Between 2013 and 2020, 9,177,313 hepatitis B cases were reported in mainland China. SARIMA(1,0,0)(0,1,1)12 was the optimal model and its residual was white noise. It was used to predict the number of hepatitis B cases from January to December 2021, and the predicted values for 2021 were within the 95% confidence interval. This study suggests that the SARIMA model simulated well based on epidemiological trends of hepatitis B in mainland China. The SARIMA model is a feasible tool for monitoring hepatitis B virus infections in mainland China.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The research of SARIMA model for prediction of hepatitis B in mainland China

et al. 2022

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“…The purpose of the feature selection was to eliminate redundant and irrelevant variables. Potential features can be selected by traditional statistical methods ( 15 ). We applied the filter method of univariate logistic regression to choose the feature subsets in which the independent variables are correlated with the dependent variable in the original data structure.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have used logistic regression or Cox proportional hazards regression models to establish the prediction tool of HIV infection among MSM, but performance is not great due to the problems of data structure which are often non-linear, abnormal, and heterogeneous ( 11 – 14 ). Compared to the above traditional models, the machine learning algorithm provides a new method to construct models, since it can balance the deviation and variance of data ( 15 ). Nowadays, machine learning has been widely applied in the medical field, mainly reflected in medical auxiliary diagnosis and classification prediction, such as image-based cancer diagnostics ( 16 , 17 ).…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Jiang

et al. 2022

Front. Public Health

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BackgroundContinuously growing of HIV incidence among men who have sex with men (MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for innovative strategies to improve the implementation of HIV prevention. The use of machine learning algorithms is an increasing tendency in disease diagnosis prediction. We aimed to develop and validate machine learning models in predicting HIV infection among MSM that can identify individuals at increased risk of HIV acquisition for transmission-reduction interventions.MethodsWe extracted data from MSM sentinel surveillance in Zhejiang province from 2018 to 2020. Univariate logistic regression was used to select significant variables in 2018–2019 data (P < 0.05). After data processing and feature selection, we divided the model development data into two groups by stratified random sampling: training data (70%) and testing data (30%). The Synthetic Minority Oversampling Technique (SMOTE) was applied to solve the problem of unbalanced data. The evaluation metrics of model performance were comprised of accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic curve (AUC). Then, we explored three commonly-used machine learning algorithms to compare with logistic regression (LR), including decision tree (DT), support vector machines (SVM), and random forest (RF). Finally, the four models were validated prospectively with 2020 data from Zhejiang province.ResultsA total of 6,346 MSM were included in model development data, 372 of whom were diagnosed with HIV. In feature selection, 12 variables were selected as model predicting indicators. Compared with LR, the algorithms of DT, SVM, and RF improved the classification prediction performance in SMOTE-processed data, with the AUC of 0.778, 0.856, 0.887, and 0.942, respectively. RF was the best-performing algorithm (accuracy = 0.871, precision = 0.960, recall = 0.775, F-measure = 0.858, and AUC = 0.942). And the RF model still performed well on prospective validation (AUC = 0.846).ConclusionMachine learning models are substantially better than conventional LR model and RF should be considered in prediction tools of HIV infection in Chinese MSM. Further studies are needed to optimize and promote these algorithms and evaluate their impact on HIV prevention of MSM.

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“…Yashik et al [2]: Two machine learning methods were applied: SVM and neural network. Yin et al [3]: DT, RF, and AdaBoost with DT (AdaBoost) were applied. AdaBoost showed the highest accuracy = 92.8%, precision = 91.5%, recall = 94.4%, F − 1 = 93.0%, and AUC = 96%.…”

Section: Related Workmentioning

confidence: 99%

Application of machine learning and deep learning for the prediction of HIV/AIDS

Alehegn¹

2022

HIV & AIDS Review

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Introduction: Nowadays human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) is very dangerous. HIV targets the resistant system and weakens people's denial against many contaminations and some kinds of cancer. As the virus breaks up and impairs the function of immunity, infected people gradually become immunodeficient. Both deep learning and machine learning models play a great role in the prediction of diseases. The function of immunity is CD4 cell count. In this study both the machine learning and deep learning algorithms were applied. Material and methods:In this paper the data were collected from data world. Support vector machine, random forest, naïve Bayes, gated recurrent unit, and long short-term memory were used to fit the incidence data. The performance of the model was evaluated by accuracy, precision, sensitivity, and F-score with respective errors.Results: Based on the evaluation, deep learning models achieve better results in the metrics of accuracy, precision, and F-score than machine learning models. But in sensitivity metrics machine learning models achieve better result than deep learning. Machine learning algorithms SVM, RF, and NB provide accuracy of 89.00%, 87.00%, and 86.94%; precision of 75.89%, 74.97%, and 75.78%; sensitivity of 87.96%, 84.00%, and 84.12%; F-score of 82.87%, 80.03%, and 79.05%, respectively. LSTM, GRU provides accuracy of 97.65%, 96.00%, precision of 77.35%, 84.00%, sensitivity of 87.93%, 82.98%, and F-score of 82.03%, 83.20%, respectively. Conclusions:The possibility survival of the illness is less than no illness. The existence of TB negative is higher than TB positive. In the machine learning model SVM provides better sensitivity with 87.96%, long short-term memory provides accuracy of 97.65%, precision of 77.35%, sensitivity of 87.93%, and F-score of 82.03%.

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A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

Cited by 5 publications

References 41 publications

The research of SARIMA model for prediction of hepatitis B in mainland China

The research of SARIMA model for prediction of hepatitis B in mainland China

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Application of machine learning and deep learning for the prediction of HIV/AIDS

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