Predicting parental Human papillomavirus vaccine hesitancy: development and internal validation of machine learning models
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Objectives
Cervical cancer is Cameroon’s second most common cancer among women. However, high parental Human papillomavirus (HPV) vaccine hesitancy impedes elimination, and no validated predictive tool exists. This study aimed to develop and internally validate machine learning models predicting parental HPV vaccine hesitancy in the Buea Health District and identify key predictors.
Methods
This secondary analysis used cross-sectional survey data from 1,156 parents of children aged 9–18 years. Hesitancy was derived from self-reported vaccination status and intention. Twenty-six predictor groups were screened via group LASSO (17 retained for logistic regression; all 26 for tree-based models). Data were split 80/20 into training (n=925) and test (n=231) sets. Logistic regression, random forest, and XGBoost were tuned via repeated cross-validation while thresholds were set by Youden’s J statistic. Discrimination, calibration, and Brier score were assessed on the test set. Predictions were interpreted using SHAP.
Results
All models showed comparable discrimination (AUC-ROC 0.841–0.870). XGBoost had the highest AUC (0.870, vs 0.869 for logistic regression, DeLong p=0.965); logistic regression had the highest sensitivity (73.1%), F1-score (74.9%), and lowest Brier score (0.1435). Calibration slopes exceeded 1 (1.12–1.46) across models. Insufficient vaccine information, perceived vaccine unsafety, and distrust in the Ministry of Health were the dominant predictors.
Discussion
The three models achieved comparable, clinically meaningful discrimination, and algorithmic complexity did not improve prediction over standard regression. Hesitancy was more strongly predicted by information access and institutional trust than sociodemographic disadvantage.
Conclusion
These findings support trust-building engagement over broad demographic campaigns.
What is already known on this topic
Parental HPV vaccine hesitancy is a major barrier to HPV vaccination, but predictive modelling evidence from sub-Saharan Africa is limited and no internally validated model for parental HPV vaccine hesitancy has been developed in the region.
What this study adds
We developed and internally validated logistic regression, random forest and XGBoost models for predicting parental HPV vaccine hesitancy, with all three showing comparable discrimination (AUC-ROC 0.841–0.870) and logistic regression achieving the lowest Brier score and highest sensitivity and F1-score. Insufficient vaccine information, perceived vaccine unsafety and distrust in the Ministry of Health were the dominant predictors across the models and interpretability analyses.
How this study might affect research, practice or policy
The findings support the development and external validation of simple, interpretable prediction tools to identify parents who may benefit from targeted, trust-building vaccine communication and community engagement to support HPV vaccination and cervical cancer prevention.
