Setegn Gizachew Mulu, Dejene Belayneh Endalamaw
Department of Computer Science, Debark University, Debark, 90, Ethiopia.
University of Gondar, Gondar, 196, Ethiopia.
Ital J Pediatr. 2025 Mar 24;51(1):91. doi: 10.1186/s13052-025-01892-1.
Child nutrition in Ethiopia is a significant concern, particularly for preschool-aged children. Children must have a varied diet to ensure they receive all the essential nutrients for good health. Unfortunately, many children in Ethiopia lack access to a range of foods, which can lead to malnutrition and other health issues. While machine learning (ML) has the potential to analyse extensive datasets, the lack of transparency in these models can impede their effectiveness in real-world applications, especially in public health. This research aims to enhance machine learning models by integrating Explainable AI (XAI) methods to more accurately predict the level of dietary diversity in Ethiopian preschool children.
To Improve the ML Model for Predicting the Level of Dietary Diversity among Ethiopian Preschool Children. We employed an ensemble ML approach with XAI. The Ethiopian demographic health survey collected a dataset consisting of dietary information and relevant socioeconomic variables. The data were preprocessed to obtain quality data that are suitable for the ensemble ML algorithms to develop a model. We applied filter (chi-square and mutual information) and wrapper (sequential backwards) feature selection methods to identify the most influential factors for dietary diversity (DD). Ethiopia demographic health survey (from 2011 to 2019). Datasets were used. We developed a predictive model using a decision tree, random forest, gradient boosting, light gradient boosting, CatBoost, and XGBClassifier. We evaluated it using accuracy, precision, recall, F1_score, and receiver operating characteristic (ROC)-based evaluation techniques.
The ensemble ML models exhibited robust predictive performance, and light gradient boosting outperformed the other ensemble ML algorithms by 95.3%. The explainability of the Light Gradient Boosting Ensemble Model was determined using Eli5 and LIME. The child's age, household wealth index, household region, source of drinking water, frequency of listening to the radio, and mother's education level were the most crucial variables for the prediction of Minimum Dietary Diversity (MDD) in Ethiopia.
The research effectively demonstrated that integrating Explainable AI with machine learning can accurately predict dietary diversity in preschoolers in Ethiopia. The results of this study have significant implications for stakeholders in child development and nutrition, as well as for policymakers and medical experts. Targeted interventions and policies to enhance the nutritional health of Ethiopian preschool children are made possible by the explainable AI model that has been constructed.
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埃塞俄比亚的儿童营养问题备受关注，尤其是学龄前儿童。儿童必须摄入多样化的饮食，以确保获得所有促进健康所需的必需营养素。不幸的是，埃塞俄比亚的许多儿童无法获得各类食物，这可能导致营养不良和其他健康问题。虽然机器学习（ML）有潜力分析大量数据集，但这些模型缺乏透明度可能会妨碍其在实际应用中的有效性，特别是在公共卫生领域。本研究旨在通过整合可解释人工智能（XAI）方法来增强机器学习模型，以更准确地预测埃塞俄比亚学龄前儿童的饮食多样性水平。
为改进预测埃塞俄比亚学龄前儿童饮食多样性水平的机器学习模型。我们采用了带有XAI的集成机器学习方法。埃塞俄比亚人口与健康调查收集了一个由饮食信息和相关社会经济变量组成的数据集。对数据进行预处理，以获得适合集成机器学习算法开发模型的高质量数据。我们应用过滤（卡方检验和互信息）和包装（顺序向后）特征选择方法来确定对饮食多样性（DD）最具影响力的因素。使用了埃塞俄比亚2011年至2019年的人口与健康调查数据集。我们使用决策树、随机森林、梯度提升、轻梯度提升、CatBoost和XGBClassifier开发了一个预测模型。我们使用准确率、精确率、召回率、F1分数和基于接收者操作特征（ROC）的评估技术对其进行评估。
集成机器学习模型表现出强大的预测性能，轻梯度提升的表现比其他集成机器学习算法高出95.3%。使用Eli5和LIME确定了轻梯度提升集成模型的可解释性。儿童年龄、家庭财富指数、家庭所在地区、饮用水来源、收听广播的频率以及母亲的教育水平是预测埃塞俄比亚最低饮食多样性（MDD）的最关键变量。
该研究有效地证明了将可解释人工智能与机器学习相结合能够准确预测埃塞俄比亚学龄前儿童的饮食多样性。本研究结果对儿童发展和营养领域的利益相关者以及政策制定者和医学专家具有重要意义。所构建的可解释人工智能模型使得制定有针对性的干预措施和政策以改善埃塞俄比亚学龄前儿童的营养健康成为可能。
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