Evidence map›Paper›PMID 39157521›Full record

ArticleFrontiers in public health2024

Predictive modeling and socioeconomic determinants of diarrhea in children under five in the Amhara Region, Ethiopia.

Abdulaziz Kebede Kassaw, Ayana Alebachew Muluneh, Ebrahim Msaye Assefa, Ali Yimer

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Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Abdulaziz Kebede KassawDepartment of Health Informatics, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Ayana Alebachew MulunehDepartment of Health Informatics, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Ebrahim Msaye AssefaDepartment of Pre-clerkship, College of Medicine and Health Science, Wollo University, Dessie, Ethiopia.
Ali YimerDepartment of Public Health, College of Health Sciences, Woldia University, Woldia, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diarrheal disease, characterized by high morbidity and mortality rates, continues to be a serious public health concern, especially in developing nations such as Ethiopia. The significant burden it imposes on these countries underscores the importance of identifying predictors of diarrhea. The use of machine learning techniques to identify significant predictors of diarrhea in children under the age of 5 in Ethiopia's Amhara Region is not well documented. Therefore, this study aimed to clarify these issues. Methods: This study's data have been extracted from the Ethiopian Population and Health Survey. We have applied machine learning ensemble classifier models such as random forests, logistic regression, K-nearest neighbors, decision trees, support vector machines, gradient boosting, and naive Bayes models to predict the determinants of diarrhea in children under the age of 5 in Ethiopia. Finally, Shapley Additive exPlanation (SHAP) value analysis was performed to predict diarrhea. Result: Among the seven models used, the random forest algorithm showed the highest accuracy in predicting diarrheal disease with an accuracy rate of 81.03% and an area under the curve of 86.50%. The following factors were investigated: families who had richest wealth status (log odd of -0.04), children without a history of Acute Respiratory Infections (ARIs) (log odd of -0.08), mothers who did not have a job (log odd of -0.04), children aged between 23 and 36 months (log odd of -0.03), mothers with higher education (log odds ratio of -0.03), urban dwellers (log odd of -0.01), families using electricity as cooking material (log odd of -0.12), children under 5 years of age living in the Amhara region of Ethiopia who did not show signs of wasting, children under 5 years of age who had not taken medications for intestinal parasites unlike their peers and who showed a significant association with diarrheal disease. Conclusion: We recommend implementing programs to reduce the incidence of diarrhea in children under the age of 5 in the Amhara region. These programs should focus on removing socioeconomic barriers that impede mothers' access to wealth, a favorable work environment, cooking fuel, education, and healthcare for their children.

Indexed as

DiarrheaMachine LearningSocioeconomic FactorsAdultChild, PreschoolEthiopiaFemaleHealth SurveysHumansInfantInfant, NewbornLogistic ModelsMaleRisk FactorsdiarrheaEthiopiamachine-learningprediction modelingunder five children

Identifiers

PMID39157521
PMCPMC11327862

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