STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Vaccines Review Article

ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach

  1. Shrutika Bhasme MCA Thakur Institute of Management Studies, Career Development & Research
  2. Ramsa Ansari MCA Thakur Institute of Management Studies, Career Development & Research
  3. Anamika Dhawan MCA Thakur Institute of Management Studies, Career Development & Research

Abstract

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

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