PDF(Pubmed)
Abstract：
BACKGROUND: The provincial-level sero-survey was launched to learn the updated seroprevalence of hepatitis B virus (HBV) infection in the general population aged 1-69 years in Chongqing and to assess the risk factors for HBV infection to effectively screen persons with chronic hepatitis B (CHB).
METHODS: A total of 1828 individuals aged 1-69 years were investigated, and hepatitis B surface antigen (HBsAg), antibody to HBsAg (HBsAb), and antibody to B core antigen (HBcAb) were detected. Logistic regression and three machine learning (ML) algorithms, including random forest (RF), support vector machine (SVM), and stochastic gradient boosting (SGB), were developed for analysis.
RESULTS: The HBsAg prevalence of the total population was 3.83%, and among persons aged 1-14 years and 15-69 years, it was 0.24% and 4.89%, respectively. A large figure of 95.18% (770/809) of adults was unaware of their occult HBV infection. Age, region, and immunization history were found to be statistically associated with HBcAb prevalence with a logistic regression model. The prediction accuracies were 0.717, 0.727, and 0.725 for the proposed RF, SVM, and SGB models, respectively.
CONCLUSIONS: The logistic regression integrated with ML models could helpfully screen the risk factors for HBV infection and identify high-risk populations with CHB.
METHODS: A total of 1828 individuals aged 1-69 years were investigated, and hepatitis B surface antigen (HBsAg), antibody to HBsAg (HBsAb), and antibody to B core antigen (HBcAb) were detected. Logistic regression and three machine learning (ML) algorithms, including random forest (RF), support vector machine (SVM), and stochastic gradient boosting (SGB), were developed for analysis.
RESULTS: The HBsAg prevalence of the total population was 3.83%, and among persons aged 1-14 years and 15-69 years, it was 0.24% and 4.89%, respectively. A large figure of 95.18% (770/809) of adults was unaware of their occult HBV infection. Age, region, and immunization history were found to be statistically associated with HBcAb prevalence with a logistic regression model. The prediction accuracies were 0.717, 0.727, and 0.725 for the proposed RF, SVM, and SGB models, respectively.
CONCLUSIONS: The logistic regression integrated with ML models could helpfully screen the risk factors for HBV infection and identify high-risk populations with CHB.
摘要：
背景：启动了省级血清调查，以了解重庆市1-69岁普通人群中乙型肝炎病毒（HBV）感染的最新血清阳性率，并评估HBV感染的危险因素，以有效筛查慢性乙型肝炎（CHB）患者。
方法：共调查了1828名1-69岁的个体，和乙型肝炎表面抗原（HBsAg），HBsAg抗体（HBsAb），检测B核心抗原抗体(HBcAb)。逻辑回归和三种机器学习(ML)算法，包括随机森林(RF)，支持向量机(SVM)，和随机梯度提升(SGB)，是为分析而开发的。
结果：总人口的HBsAg患病率为3.83％，在1-14岁和15-69岁的人中，分别为0.24%和4.89%，分别。95.18％（770/809）的成年人没有意识到他们隐匿性HBV感染。年龄,区域,通过logistic回归模型,发现免疫史与HBcAb患病率有统计学关联.拟议RF的预测精度为0.717、0.727和0.725，SVM,和SGB模型，分别。
结论：与ML模型整合的逻辑回归可以帮助筛选HBV感染的危险因素，并确定CHB的高危人群。
方法：共调查了1828名1-69岁的个体，和乙型肝炎表面抗原（HBsAg），HBsAg抗体（HBsAb），检测B核心抗原抗体(HBcAb)。逻辑回归和三种机器学习(ML)算法，包括随机森林(RF)，支持向量机(SVM)，和随机梯度提升(SGB)，是为分析而开发的。
结果：总人口的HBsAg患病率为3.83％，在1-14岁和15-69岁的人中，分别为0.24%和4.89%，分别。95.18％（770/809）的成年人没有意识到他们隐匿性HBV感染。年龄,区域,通过logistic回归模型,发现免疫史与HBcAb患病率有统计学关联.拟议RF的预测精度为0.717、0.727和0.725，SVM,和SGB模型，分别。
结论：与ML模型整合的逻辑回归可以帮助筛选HBV感染的危险因素，并确定CHB的高危人群。
