{Reference Type}: Journal Article
{Title}: Identification of associated risk factors for serological distribution of hepatitis B virus via machine learning models.
{Author}: Yao N;Liu Y;Xu J;Wang Q;Zhou Q;Wang Y;Yi D;Wu Y;
{Journal}: BMC Infect Dis
{Volume}: 24
{Issue}: 1
{Year}: 2024 Jan 9
{Factor}: 3.667
{DOI}: 10.1186/s12879-023-08911-8
{Abstract}: <B>BACKGROUND: </B>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).<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>The logistic regression integrated with ML models could helpfully screen the risk factors for HBV infection and identify high-risk populations with CHB.