Prediction of COVID 19 vaccine uptake among Nigerian women using supervised machine learning based on 2024 Demographic and Health Survey data

Abstract Background COVID-19 vaccination remains a key strategy for reducing severe illness and mortality, yet uptake remains uneven in Nigeria. Although prior studies have highlighted multiple predictors of vaccine uptake among women, most rely on traditional analytical approaches, limiting their ability to capture complex, non-linear drivers of vaccine behaviour. Applying robust, data-driven machine learning methods is therefore essential to more accurately identify key predictors of vaccine uptake and to inform targeted, equitable vaccination interventions. Methods This study employed machine learning techniques using data from the 2024 Nigeria Demographic and Health Survey. A weighted sample of 36,205 women aged 15–49 years was considered using Python software. Sociodemographic, household, reproductive, health-system, and media-exposure variables were considered as features. After preprocessing, the dataset was split into training (80%) and testing (20%) sets. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied. Multiple supervised machine-learning algorithms, namely Logistic Regression, Decision Tree Classifier, Random Forest, Gradient Boosting Machines, extreme gradient boosting, CatBoost, Support Vector Machines, K-Nearest Neighbors, and Artificial Neural Networks, were applied. Model effectivenesswas evaluated using accuracy and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) as primary metrics. The best-performing model was interpreted using SHapley Additive exPlanations (SHAP) to identify the most influential predictors of vaccine uptake. Results Overall, 27.6% of women reported receiving at least one dose of a COVID-19 vaccine (95% CI: 27.14–28.06). Ten-fold cross-validation with SMOTE produced the best results, emphasizing the significance of addressing class. Random Forest achieved the highest performance (accuracy = 75.1%, AUC = 82.8%). SHAP-based interpretation of the Random Forest model identified older age (35–49 years), region of residence (notably outside the North West and South East, particularly South West), and marital status (not never in union) as key positive contributors to vaccine uptake, while no radio exposure reduced the predicted likelihood of vaccination. Conclusion This study identified low coverage of COVID-19 vaccine uptake among Nigerian women. Policymakers should adopt targeted, equity-focused vaccination strategies that prioritise underserved regions and socioeconomically disadvantaged women. Integrating COVID-19 vaccination into routine maternal and reproductive health services, expanding community-based and mobile outreach, and strengthening radio-based and culturally tailored communication campaigns are essential to improve coverage.