Identification of associated risk factors for serological distribution of hepatitis B virus via machine learning models.
BMC Infect Dis
; 24(1): 66, 2024 Jan 09.
Article
en En
| MEDLINE
| ID: mdl-38195403
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Hepatitis B Crónica
/
Hepatitis B
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Humans
Idioma:
En
Revista:
BMC Infect Dis
Asunto de la revista:
DOENCAS TRANSMISSIVEIS
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Reino Unido