ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach - Shrutika Bhasme; Ramsa Ansari; Anamika Dhawan (International Journal of Vaccines, 2026)
Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated or unvaccinated. Among various algorithms tested—including Logistic Regression, Decision Trees, Support Vector Machines, and Random Forest—the Random Forest classifier demonstrated superior performance with 89.6% accuracy, 0.88 precision, 0.90 recall, and 0.89 F1-score. Feature importance analysis revealed that parental awareness, family income, maternal education level, distance to healthcare centers, and availability of immunization facilities were the most significant predictors. These findings provide actionable, data-driven insights for healthcare policymakers to design targeted interventions aimed at improving immunization rates in underserved rural areas, highlighting the potential of machine learning in strengthening public health strategies through early identification of at-risk children and optimized resource allocation.
Keywords: Childhood Immunization; Machine Learning; healthcare access; predictive analytics; random forest; rural india