用机器学习预测埃塞俄比亚孕妇营养状况，准确率达97.87%
Data-Driven Prediction of Maternal Nutritional Status in Ethiopia Using Ensemble Machine Learning Models
- 融合多种集成学习模型，从1.8万份健康调查数据中挖掘营养风险因素
- 随机森林模型在四类营养状态分类上准确率超97%，AUC达99.86%
- 结果可为基层医疗和政策制定提供早期预警，助力改善孕产妇营养
埃塞俄比亚孕产妇营养不良是重大公共卫生挑战，增加母婴不良结局风险。传统统计方法难以捕捉营养状况的复杂多维决定因素。本研究基于2005-2020年埃塞俄比亚人口与健康调查数据（18,108条记录，30个社会人口与健康属性），采用集成机器学习技术构建预测模型。数据预处理包括缺失值处理、归一化及SMOTE平衡，结合特征选择识别关键预测因子。应用XGBoost、随机森林、CatBoost、AdaBoost等监督集成算法进行营养状态分类。其中，随机森林表现最佳，将女性分为正常、轻度营养不良、重度营养不良和超重四类，准确率97.87%、精确率97.88%、召回率97.87%、F1分数97.87%、ROC AUC 99.86%。结果表明集成学习能有效挖掘复杂数据中的隐藏模式，为营养风险的早期识别提供及时洞察，对医疗提供者、政策制定者及研究人员具有实践意义，支持数据驱动策略以改善埃塞俄比亚孕产妇营养与健康结局。
原文摘要 · Abstract (English)
Malnutrition among pregnant women is a major public health challenge in Ethiopia, increasing the risk of adverse maternal and neonatal outcomes. Traditional statistical approaches often fail to capture the complex and multidimensional determinants of nutritional status. This study develops a predictive model using ensemble machine learning techniques, leveraging data from the Ethiopian Demographic and Health Survey (2005-2020), comprising 18,108 records with 30 socio-demographic and health attributes. Data preprocessing included handling missing values, normalization, and balancing with SMOTE, followed by feature selection to identify key predictors. Several supervised ensemble algorithms including XGBoost, Random Forest, CatBoost, and AdaBoost were applied to classify nutritional status. Among them, the Random Forest model achieved the best performance, classifying women into four categories (normal, moderate malnutrition, severe malnutrition, and overnutrition) with 97.87% accuracy, 97.88% precision, 97.87% recall, 97.87% F1-score, and 99.86% ROC AUC. These findings demonstrate the effectiveness of ensemble learning in capturing hidden patterns from complex datasets and provide timely insights for early detection of nutritional risks. The results offer practical implications for healthcare providers, policymakers, and researchers, supporting data-driven strategies to improve maternal nutrition and health outcomes in Ethiopia.
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