Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population

Abstract

Despite a decline in the prevalence of hepatitis B in China, the disease burden remains high. Large populations unaware of infection risk often fail to meet the ideal treatment window, resulting in poor prognosis. The purpose of this study was to develop and evaluate models identifying high-risk populations who should be tested for hepatitis B surface antigen. Data came from a large community-based health screening, including 97,173 individuals, with an average age of 54.94. A total of 33 indicators were collected as model predictors, including demographic characteristics, routine blood indicators, and liver function. Borderline-Synthetic minority oversampling technique (SMOTE) was conducted to preprocess the data and then four predictive models, namely, the extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and logistic regression (LR) algorithms, were developed. The positive rate of hepatitis B surface antigen (HBsAg) was 8.27%. The area under the receiver operating characteristic curves for XGBoost, RF, DT, and LR models were 0.779, 0.752, 0.619, and 0.742, respectively. The Borderline-SMOTE XGBoost combined model outperformed the other models, which correctly predicted 13,637/19,435 cases (sensitivity 70.8%, specificity 70.1%), and the variable importance plot of XGBoost model indicated that age was of high importance. The prediction model can be used to accurately identify populations at high risk of hepatitis B infection that should adopt timely appropriate medical treatment measures.

Exploratory Study of Some Machine Learning Techniques to Classify the Patient Treatment

“…These combination techniques showed that there are significant differences in symptomatic entropy between patients with type II and type I hypertension. The experimental study of various classifiers techniques, such as XGBoost, was conducted by [32] to predict fallsnon falls of Parkinson Diseases. Therefore, clinical, demographic, and neuroimaging data used in this study were obtained from Medical Centres, University of Michigan, and Tel Aviv Sourasky Medical Center.…”

Recent Advances in Understanding, Diagnosing, and Treating Hepatitis B Virus Infection

“…Additionally, an ANN model was demonstrated as effective in diagnosing liver fibrosis reversion for chronic HBV-induced liver fibrosis patients [ 97 ]. Machine learning algorithms have been recently applied for the accurate identification of individuals at high risk of HBV infection and the developed model was proposed as an improvement for the detection rate of positive HBsAg [ 98 ]. Furthermore, the ability of ANN to accurately predict HBsAg seroclearance in HBeAg-negative CHB patients was recently demonstrated.…”

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

“…Previous studies have utilized XGBoost and shown significant results for predicting hepatitis B virus infection, gestational diabetes mellitus of early pregnant women, future blood glucose level of T1D patients, and coronary artery calcium score (CACS). A hepatitis B virus infection prediction based on XGBoost and Borderline-Synthetic minority oversampling technique (Borderline-SMOTE) was developed by Wang et al (2019) to identify high-risk populations in China [13]. The result revealed that their model performed better than other models, achieving an area under the receiver operating characteristic curves (AUC) of 0.782.…”

“…After selecting the best feature sets by utilizing the GA from the training data, the XGBoost is used to learn and generate the robust prediction model. Previous studies have reported the advantage of using XGBoost for predicting hepatitis B virus infection [13], gestational diabetes mellitus of early pregnant women [14], future blood glucose level of T1D patients [15], coronary artery calcium score (CACS) [16], and heart disease prediction [17]. XGBoost was proposed by Chen and Guestrin and is a scalable supervised machine learning algorithm based on the improvement of gradient boosting decision trees (GBDT) and used for regression and classification problems [12].…”

“…Extreme gradient boosting (XGBoost) is supervised machine learning algorithm based on the improvement of gradient boosting trees that can prevent overfitting [12]. Previous researchers have reported the advantage of XGBoost as classification model for predicting hepatitis B virus infection [13], gestational diabetes mellitus of early pregnant women [14], future blood glucose level of type 1 diabetes (T1D) patients [15], coronary artery calcium score (CACS) [16], and heart disease prediction [17]. However, in the machine learning research, the performance of the classification model may be influenced by unrelated attributes or features [18,19].…”