Prevalence and predictors of acute respiratory infection among children under-five years in Tigray regional state, northern Ethiopia: a cross sectional study

Gebrerufael, Gebru Gebremeskel; Hagos, Bsrat Tesfay

doi:10.1186/s12879-023-08701-2

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…If a mother has any symptoms of ARI while going to these services, she may bring her child for medical attention. Similar findings corroborate the conclusions ( 44 ).…”

Section: Discussionsupporting

confidence: 92%

Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa

Yehuala,

Agimas,

Derseh

et al. 2024

Front. Public Health

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BackgroundAcute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children under the age 5 in sub-Saharan Africa (SSA) using machine learning models.MethodsThe sub-Saharan African countries’ demographic health survey was the source of the dataset. We used a weighted sample of 16,832 under-five children in this study. The data were processed using Python (version 3.9), and machine learning models such as extreme gradient boosting (XGB), random forest, decision tree, logistic regression, and Naïve Bayes were applied. In this study, we used evaluation metrics, including the AUC ROC curve, accuracy, precision, recall, and F-measure, to assess the performance of the predictive models.ResultIn this study, a weighted sample of 16,832 under-five children was used in the final analysis. Among the proposed machine learning models, the random forest (RF) was the best-predicted model with an accuracy of 88.89%, a precision of 89.5%, an F-measure of 83%, an AUC ROC curve of 95.8%, and a recall of 77.6% in predicting the absence of mothers’ healthcare-seeking behavior for ARIs. The accuracy for Naïve Bayes was the lowest (66.41%) when compared to other proposed models. No media exposure, living in rural areas, not breastfeeding, poor wealth status, home delivery, no ANC visit, no maternal education, mothers’ age group of 35–49 years, and distance to health facilities were significant predictors for the absence of mothers’ healthcare-seeking behaviors for ARIs. On the other hand, undernourished children with stunting, underweight, and wasting status, diarrhea, birth size, married women, being a male or female sex child, and having a maternal occupation were significantly associated with good maternal healthcare-seeking behaviors for ARIs among under-five children.ConclusionThe RF model provides greater predictive power for estimating mothers’ healthcare-seeking behaviors based on ARI risk factors. Machine learning could help achieve early prediction and intervention in children with high-risk ARIs. This leads to a recommendation for policy direction to reduce child mortality due to ARIs in sub-Saharan countries.

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“…If a mother has any symptoms of ARI while going to these services, she may bring her child for medical attention. Similar findings corroborate the conclusions ( 44 ).…”

Section: Discussionsupporting

confidence: 92%

Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa

Yehuala,

Agimas,

Derseh

et al. 2024

Front. Public Health

View full text Add to dashboard Cite

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“…The most used conventional statistical methods are often limited in identifying unusual patterns and hidden relationships in Big data. The majority of previous studies used local clinical data in a single city or town, with a sample size of fewer than 500 cases or records, and were primarily concerned with a small number of predicting factors [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under-fives using demographic and health survey insights

Kalayou,

Kassaw,

Shiferaw

2024

BMC Infect Dis

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Background A dearth of studies showed that infectious diseases cause the majority of deaths among under-five children. Worldwide, Acute Respiratory Infection (ARI) continues to be the second most frequent cause of illness and mortality among children under the age of five. The paramount disease burden in developing nations, including Ethiopia, is still ARI. Objective This study aims to determine the magnitude and predictors of ARI among under-five children in Ethiopia using used state of the art machine learning algorithms. Methods Data for this study were derived from the 2016 Ethiopian Demographic and Health Survey. To predict the determinants of acute respiratory infections, we performed several experiments on ten machine learning algorithms (random forests, decision trees, support vector machines, Naïve Bayes, and K-nearest neighbors, Lasso regression, GBoost, XGboost), including one classic logistic regression model and an ensemble of the best performing models. The prediction ability of each machine-learning model was assessed using receiver operating characteristic curves, precision-recall curves, and classification metrics. Results The total ARI prevalence rate among 9501 under-five children in Ethiopia was 7.2%, according to the findings of the study. The overall performance of the ensemble model of SVM, GBoost, and XGBoost showed an improved performance in classifying ARI cases with an accuracy of 86%, a sensitivity of 84.6%, and an AUC-ROC of 0.87. The highest performing predictive model (the ensemble model) showed that the child’s age, history of diarrhea, wealth index, type of toilet, mother’s educational level, number of living children, mother’s occupation, and type of fuel they used were an important predicting factor for acute respiratory infection among under-five children. Conclusion The intricate web of factors contributing to ARI among under-five children was identified using an advanced machine learning algorithm. The child’s age, history of diarrhea, wealth index, and type of toilet were among the top factors identified using the ensemble model that registered a performance of 86% accuracy. This study stands as a testament to the potential of advanced data-driven methodologies in unraveling the complexities of ARI in low-income settings.

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Assessment of Risk Factors Associated With Acute Respiratory Infections (Aris) Among Children Under 5 Years of Age, Pakistan

Adeel,

Widiatmoko

2024

ijph

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Introduction: Acute respiratory infections account for approximately 6% of the total global burden of disease; almost double the proportion compared with the other communicable diseases. Aims: To investigate the link between risk factors & ARIs in Pakistani pediatric population under 5 with different socio-demographic profiles. Methods: Research was conducted as a cross-sectional study, utilizing the Pakistan DHS 2017-18 data, after ethical approval for the utilization of the dataset. Study variables were defined as age and gender of the child, place of residence, maternal education, wealth index, breastfeeding, and the immunization status of the child. Sample size (n = 39,799) was determined as per the inclusion and exclusion criteria. Prevalence of ARIs was estimated and the association between ARIs and socio-demographic factor and child’s immunization was examined. Results: Out of 39,799 children, 51.6% were males, 21.0 % were aged 36 – 47 months, 73.7% were the rural residents, 23% belonged to lowest wealth index quintiles. The mothers of 43.7% children were illiterate and 55.7% of the children were breastfed. ARIs proportion among the sample was 14.3%. Significant association was observed between ARIs and age (p=0.000), gender of the child (p=0.001), residence (p=0.000), economic deprivation (p=0.000), maternal education (p=0.000) and breastfeeding (p=0.000), vitamin A administration (p=0.021), BCG (p=0.008), pentavalent (p=0.008), measles (p=0.000), and pneumococcal vaccination (p=0.020). Conclusion: Socio-demographic characteristics, i.e., age and gender of the child, accommodation, economic deprivation, maternal education, breast-feeding and poor vaccination uptake were observed to be positively correlated with ARIs among children under 5 years of age in Pakistan.

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Prevalence and predictors of acute respiratory infection among children under-five years in Tigray regional state, northern Ethiopia: a cross sectional study

Cited by 4 publications

References 28 publications

Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa

Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa

Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under-fives using demographic and health survey insights

Assessment of Risk Factors Associated With Acute Respiratory Infections (Aris) Among Children Under 5 Years of Age, Pakistan

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