MACHINE LEARNING APPLICATIONS IN RURAL HEALTHCARE: PREDICTIVE MODELING FOR IMMUNIZATION COMPLETION RATES IN OGUN STATE, NIGERIA

This study investigated the application of machine learning techniques for predicting child immunization completion in Ado-Odo/Ota Local Government Area, Ogun State, Nigeria, utilizing data from 8,808 immunization records across 15 primary healthcare centers. Using a quantitative research methodology with retrospective data analysis, we developed and compared predictive models for immunization completion patterns. Three machine learning algorithms were employed based on their proven effectiveness in healthcare applications: Logistic Regression for its interpretability in clinical settings, Support Vector Machine (SVM) for handling non-linear relationships in health data, and K-Nearest Neighbours (KNN) for processing demographic variables. The study analyzed immunization completion rates using these algorithms within a comprehensive framework incorporating Principal Component Analysis for dimensionality reduction. The Logistic Regression model demonstrated superior performance with 99.77% accuracy and an MSE of 0.0023, outperforming both SVM (99.32% accuracy) and KNN (99.03% accuracy) models. Notably, socioeconomic analysis revealed an unexpected pattern where high-income households showed lower immunization completion rates compared to low and moderate-income groups. The study's findings provide valuable insights for healthcare policy development and resource allocation strategies while demonstrating the practical applicability of machine learning in enhancing immunization program effectiveness in developing nations.