ArticleScientific reports2024
Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging artificial intelligence to predict non-adherence to pediatric immunization schedules: a systematic review.European journal of pediatrics · 2026Pooled it
- Modeling Zero-Dose Children in Ethiopia: A Machine Learning Perspective on Model Performance and Predictor Variables.JMIR pediatrics and parenting · 2026Article
- Exploring the correlates of COVID-19 vaccination inequity: a global analysis using machine learning from a health economic lens.Frontiers in health services · 2026Article
- Mapping the disparities in childhood immunization status and determinants in East Africa using ordinal logistic regression analysis.Scientific reports · 2025Article
- Design a model to predict incomplete immunization among Ethiopian children using ensemble machine learning algorithms.Scientific reports · 2025Article
- Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review.JMIR nursing · 2025Article
- Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors.BMC health services research · 2025Article
- Predicting home delivery and identifying its determinants among women aged 15-49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016-2023: a machine learning algorithm.BMC public health · 2025Article
- Access Barriers to Healthcare and Full Childhood Immunization Coverage in East Africa (2015-2023): A Propensity Score Matched Analysis.Inquiry : a journal of medical care organization, provision and financingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The World Health Organization as part of the goal of universal vaccination coverage by 2030 for all individuals. The global under-five mortality rate declined from 59% in 1990 to 38% in 2019, due to high immunization coverage. Despite the significant improvements in immunization coverage, about 20 million children were either unvaccinated or had incomplete immunization, making them more susceptible to mortality and morbidity. This study aimed to identify predictors of incomplete vaccination among children under-5 years in East Africa. An analysis of secondary data from six east African countries using Demographic and Health Survey dataset from 2016 to the recent 2021 was performed. A total weighted sample of 27,806 children aged (12-35) months was included in this study. Data were extracted using STATA version 17 statistical software and imported to a Jupyter notebook for further analysis. A supervised machine learning algorithm was implemented using different classification models. All analysis and calculations were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, XGBoost, and shap packages. XGBoost classifier demonstrated the best performance with accuracy (79.01%), recall (89.88%), F1-score (81.10%), precision (73.89%), and AUC 86%. Predictors of incomplete immunization are identified using XGBoost models with help of Shapely additive eXplanation. This study revealed that the number of living children during birth, antenatal care follow-up, maternal age, place of delivery, birth order, preceding birth interval and mothers' occupation were the top predicting factors of incomplete immunization. Thus, family planning programs should prioritize the number of living children during birth and the preceding birth interval by enhancing maternal education. In conclusion promoting institutional delivery and increasing the number of antenatal care follow-ups by more than fourfold is encouraged.
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