English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Child dehydration is a significant health concern, especially among childrenunder 5 years of age who are more susceptible to diarrhea and vomiting. InAfghanistan, severe diarrhea contributes to child mortality due to dehydration.However, there is no evidence of research exploring the potential of machinelearning techniques in diagnosing dehydration in Afghan children under five. Tofill this gap, this study leveraged various classifiers such as Random Forest,Multilayer Perceptron, Support Vector Machine, J48, and Logistic Regression todevelop a predictive model using a dataset of sick children retrieved from theAfghanistan Demographic and Health Survey (ADHS). The primary objective was todetermine the dehydration status of children under 5 years. Among all theclassifiers, Random Forest proved to be the most effective, achieving anaccuracy of 91.46%, precision of 91%, and AUC of 94%. This model canpotentially assist healthcare professionals in promptly and accuratelyidentifying dehydration in under five children, leading to timelyinterventions, and reducing the risk of severe health complications. Our studydemonstrates the potential of machine learning techniques in improving theearly diagnosis of dehydration in Afghan children.

A Machine Learning Approach to Detect Dehydration in Afghan Children