Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya

2026-04-10

AI Summary

This study addresses the dual challenges of scarce high-quality data and stringent privacy requirements in predicting childhood vaccination outcomes in low-resource settings. For the first time, the tabular diffusion generative model TabSyn is applied to generate privacy-preserving synthetic data for immunization risk prediction among the Maasai population in Narok County, Kenya, without relying on real individual records. The synthetic data are subsequently used to train logistic regression and XGBoost models. Experimental results demonstrate that this approach achieves recall, precision, and F1 scores exceeding 90% across multiple vaccine prediction tasks, matching the performance of models trained on real data. The findings indicate that the method effectively balances strong privacy guarantees with high predictive utility, offering a viable solution for health analytics in data-scarce and privacy-sensitive contexts.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Voting

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Background: Limited data utilization in low-resource settings poses a barrier to the vaccine delivery ecosystem, undermining efforts to achieve equitable immunization coverage. In nomadic populations, individuals face an increased risk of missing crucial vaccination doses as children. One such population is the Maasai in Narok County, Kenya, where the absence of high-volume, quality data hampers accurate coverage estimates, impedes efficient resource allocation, and weakens the ability to deliver timely interventions. Additionally, data privacy concerns are heightened in groups with limited sensitive data. Objectives: First, we aim to identify children at risk of missing key vaccines across a large population to provide timely, evidence-based interventions that support increased vaccination coverage. Second, we aim to better protect the privacy of sensitive health data in a vulnerable population. Methods: We digitized 8 years of child vaccination records from the MOH 510 registry (n=6,913) and applied machine learning models (Logistic Regression and XGBoost) to identify children at risk. Additionally, we utilize a novel approach to tabular diffusion-based synthetic data generation (TabSyn) to protect patient privacy within the models. Results: Our findings show that classification techniques can reliably and successfully predict children at risk of missing a vaccine, with recall, precision, and F1-scores exceeding 90% for some vaccines modeled. Additionally, training these models with synthetic data rather than real data, thus preserving the privacy of individuals within the original dataset, does not lead to a loss in predictive performance. Conclusion: These results support the use of synthetic data implementation in health informatics strategies for clinics with limited digital infrastructure, enabling privacy-preserving, scalable forecasting for childhood immunization coverage.

Problem

• vaccination prediction
• synthetic data
• data privacy
• low-resource settings
• childhood immunization

Innovation

• synthetic data
• tabular diffusion
• vaccination prediction
• privacy-preserving machine learning
• low-resource settings