Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach

被引:2
|
作者
Tadese, Zinabu Bekele [1 ]
Nigatu, Araya Mesfin [2 ]
Yehuala, Tirualem Zeleke [2 ]
Sebastian, Yakub [3 ]
机构
[1] Samara Univ, Sch Publ Hlth, Coll Med & Hlth Sci, Dept Hlth Informat, Samara, Ethiopia
[2] Univ Gondar, Inst Publ Hlth, Coll Med & Hlth Sci, Dept Hlth Informat, Gondar, Ethiopia
[3] Charles Darwin Univ, Fac Sci & Technol, Dept Informat Technol, Darwin, Australia
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
关键词
MIDDLE-INCOME COUNTRIES; VACCINATION; INVESTMENT; ALGORITHMS; COVERAGE; RETURN;
D O I
10.1038/s41598-024-62641-8
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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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页数:14
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