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.

翻译：儿童脱水是一个重要的健康问题，尤其对5岁以下易患腹泻和呕吐的儿童影响显著。在阿富汗，严重腹泻因脱水导致儿童死亡，但目前尚无研究探索机器学习技术在诊断阿富汗5岁以下儿童脱水方面的潜力。为填补这一空白，本研究利用阿富汗人口健康调查（ADHS）中患病儿童数据集，采用随机森林（Random Forest）、多层感知机（Multilayer Perceptron）、支持向量机（Support Vector Machine）、J48和逻辑回归（Logistic Regression）等分类器构建预测模型，主要目标是判定5岁以下儿童的脱水状态。在所有分类器中，随机森林表现最佳，准确率达91.46%，精确率为91%，AUC为94%。该模型可辅助医疗专业人员快速准确识别5岁以下儿童的脱水状况，从而及时干预并降低严重健康并发症风险。本研究证明了机器学习技术在改善阿富汗儿童脱水早期诊断方面的潜力。