Ensemble Machine Learning Approach to Identify Determinants of Suboptimal Measles Vaccine Coverage in Grand Bassa County, Liberia
Prince L Fully1, Darius Lehyen2, Neima N Candy3, Ohandis V Harley4
1Prince L Fully, School of Public Health, College of Health Sciences, University of Liberia, Monrovia, Liberia.
2Darius Lehyen, School of Public Health, College of Health Sciences, University of Liberia, Monrovia, Liberia.
3Neima N Candy, School of Public Health, College of Health Sciences, University of Liberia, Monrovia, Liberia.
4Ohandis V Harley, School of Public Health, College of Health Sciences, University of Liberia, Monrovia, Liberia.
Manuscript received on 03 March 2026 | Revised Manuscript received on 08 March 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 20-25 | Volume-6 Issue-3 March 2026 | Retrieval Number: 100.1/ijpmh.C114306030326 | DOI: 10.54105/ijpmh.C1143.06030326
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Objectives: Using machine-learning approaches, the researchers aimed to identify and prioritise drivers of incomplete measles vaccination among children aged 12-23 months in Grand Bassa County, Liberia. Design: Cross-sectional research conducted in a community setting. Setting: Five randomly chosen districts in Grand Bassa County, Liberia, containing both urban and rural communities. Participants: 374 caregivers of infants aged 12-23 months, recruited using multistage sampling between October 2024 and February 2025. The response rate was 87.0%. Primary and secondary outcome measures: The primary outcome was MCV2 completion. MCV1 coverage and dropout rates were considered secondary outcomes. The important determinants were found using ensemble machine learning (Random Forest, XGBoost, and LightGBM) with weighted voting. Results: MCV1 coverage was 62.8% (95% CI: 59.8 to 65.8), and MCV2 coverage was 43.6% (95% CI: 40.6 to 46.6), resulting in a 30.6% dropout rate. The ensemble model attained an accuracy of 60.0% (AUC = 0.585, 95% CI: 0.545 to 0.625). The greatest predictors discovered by feature importance analysis were caregiver education (importance=0.156), distance to health facility (importance=0.142), trust in health workers (importance=0.138), and measles knowledge (importance=0.131). Conclusions: Caregiver education, geographic access, provider trust, and knowledge all substantially impact measles vaccination completion. Targeted interventions that address these characteristics might significantly increase vaccination coverage in Liberia and other low-resource countries.
Keywords: Measles Vaccination, Ensemble Learning, Liberia, Vaccine Coverage, Machine Learning, Public Health, Health Determinants, West Africa.
Scope of the Article: Public Health