Modeling the efficacy of hepatitis B vaccination in infants and post-vaccination survival analysis

Abstract

The need for the accurate prediction of the effectiveness of vaccines in infants to prevent Hepatitis B virus (HBV) infection and accurately analyze post-vaccination survival in case of infection calls for the adoption of a robust machine learning model. This study aimed to investigate an effective HBV vaccine for infants and predict the post-vaccination survival rate for infants at risk. A clinical study was carried out on the medical records of 609 cases of vaccinated infants, focusing on variables such as age, weight, gender, diagnosed symptoms and HBV vaccines administered on them. Four machine learning models were evaluated for the performances of the vaccines while Chi-square crosstab was employed to evaluate dependency of status pre- and post-vaccination. Aside from descriptive analyses of demographic variables, Kaplan-Meier method was also employed to predict the post-vaccination survival rate. Our findings indicate that Logistic Regression and Support Vector Machine models have moderate accuracy (77%) and high precision for positive cases but struggle with negative cases, similar to Decision Tree. The Random Forest model performs better overall (81% accuracy), with balanced precision and recall, excelling in positive case predictions. Feature importance analysis shows age and weight as key predictors of HBV status, with the Penta1 + Pneumo1 + Vpo1 + Rota1 vaccine as the most dependable for HBV. Findings suggest that higher vaccine doses may correlate with lower infection rates, and Kaplan-Meier analysis estimated a 0.05 chance of post-vaccination survival from HBV infection after 100 days.