A Machine Learning Approach to Detect Dehydration in Afghan Children
Ziaullah Momand Debajyoti Pal Pornchai Mongkolnam Jonathan H. Chan
Child dehydration is a significant health concern, especially among children
under 5 years of age who are more susceptible to diarrhea and vomiting. In
Afghanistan, severe diarrhea contributes to child mortality due to dehydration.
However, there is no evidence of research exploring the potential of machine
learning techniques in diagnosing dehydration in Afghan children under five. To
fill this gap, this study leveraged various classifiers such as Random Forest,
Multilayer Perceptron, Support Vector Machine, J48, and Logistic Regression to
develop a predictive model using a dataset of sick children retrieved from the
Afghanistan Demographic and Health Survey (ADHS). The primary objective was to
determine the dehydration status of children under 5 years. Among all the
classifiers, Random Forest proved to be the most effective, achieving an
accuracy of 91.46%, precision of 91%, and AUC of 94%. This model can
potentially assist healthcare professionals in promptly and accurately
identifying dehydration in under five children, leading to timely
interventions, and reducing the risk of severe health complications. Our study
demonstrates the potential of machine learning techniques in improving the
early diagnosis of dehydration in Afghan children.