Predicting parental Human papillomavirus vaccine hesitancy: development and internal validation of machine learning models
- 1Global Health Trials Unit, Liverpool School of Tropical Medicine, United Kingdom
Abstract
ObjectivesCervical cancer is Cameroons 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.
MethodsThis 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 Youdens J statistic. Discrimination, calibration, and Brier score were assessed on the test set. Predictions were interpreted using SHAP.
ResultsAll 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.
DiscussionThe 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.
ConclusionThese findings support trust-building engagement over broad demographic campaigns.
What is already known on this topicParental 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 addsWe 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 policyThe 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.
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BibTeX
@misc{alabi2026predicting,
title = {{Predicting parental Human papillomavirus vaccine hesitancy: development and internal validation of machine learning models}},
author = {I. Oluwamayomikun Alabi and D. Biftu Bekalo and C. Kartsonaki and V. N. Agbor},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.26363393},
url = {https://www.medrxiv.org/content/10.64898/2026.09.18.26363393v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Predicting parental Human papillomavirus vaccine hesitancy: development and internal validation of machine learning models
AU - I. Oluwamayomikun Alabi
AU - D. Biftu Bekalo
AU - C. Kartsonaki
AU - V. N. Agbor
PY - 2026
DA - 2026/09/18
PB - medRxiv
DO - 10.64898/2026.09.18.26363393
UR - https://www.medrxiv.org/content/10.64898/2026.09.18.26363393v1
AB - ObjectivesCervical cancer is Cameroons 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. MethodsThis 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 Youdens J statistic. Discrimination, calibration, and Brier score were assessed on the test set. Predictions were interpreted using SHAP. ResultsAll 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. DiscussionThe 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. ConclusionThese findings support trust-building engagement over broad demographic campaigns. What is already known on this topicParental 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 addsWe 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 policyThe 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.
ER -