• Abdelaziz Hendy, Sally Mohammed Farghaly Abdelaliem, Hosny Maher Sultan, Shorok Hamed Alahmedi, Rasha Kadri Ibrahim, Eman Mohamed Ebrahim Abdelrazek, Masani Abdelbagi Ahmed Elmahdy, and Ahmed Hendy.
• Pediatric Nursing Department, Faculty Nursing, Ain Shams University, Cairo, Egypt; Department of Nursing Management and Education, College of Nursing, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia. Electronic address: Abdelaziz.hendy@nursing.asu.edu.eg.
• Nutrition. 2024 Nov 12; 131: 112631112631.

IntroductionMalnutrition, particularly wasting, continues to be a significant public health issue among children under five years in Egypt. Despite global advancements in child health, the prevalence of wasting remains a critical concern. This study employs machine learning techniques to identify and analyze the determinants of wasting in this population.AimTo evaluate the prevalence of wasting among children under five years in Egypt and identify key factors associated with wasting using machine learning models.MethodsThis study is based on secondary data sourced from the Demographic and Health Surveys (DHS), conducted in 2005, 2008, and 2014. Six machine learning classifiers (XGBoost, Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbor, and Decision Tree) were applied to the dataset. The study included children under five years of age, focusing on nutritional status, maternal health, and socio-economic factors. The dataset was cleaned, preprocessed, encoded using one-hot encoding, and split into training (70%) and test (30%) sets. Additionally, k-fold cross-validation and the StandardScaler function from Scikit-learn were used. Performance metrics such as accuracy, precision, recall, F1 score, and ROC-AUC were used to evaluate and compare the algorithms.ResultsIt was observed that 76.2% of the children in the dataset have normal nutritional status. Furthermore, 5.2% were found to be suffering from wasting (1.7% experiencing severe wasting and 3.5% moderate wasting), with notable regional disparities. The XGBoost model outperformed other models. Its efficiency metrics include an accuracy of 94.8%, precision of 94.7%, recall of 94.7%, F1 score of 94.7%, and an ROC-AUC of 99.4%. These results indicate that XGBoost was highly effective in predicting wasting.ConclusionMachine learning techniques, particularly XGBoost, show significant potential for improving the classification of nutritional status and addressing wasting among children in Egypt. However, the limitations in simpler models highlight the need for further research to refine predictive tools and develop targeted interventions. Addressing the identified determinants of wasting can contribute to more effective public health strategies.Copyright © 2024 Elsevier Inc. All rights reserved.

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