利用机器学习和深度学习预测尼泊尔五岁以下儿童贫血
Predicting Anemia Among Under-Five Children in Nepal Using Machine Learning and Deep Learning
AI总结：
利用机器学习和深度学习预测尼泊尔五岁以下儿童贫血，发现逻辑回归在召回率和F1分数上表现最佳，支持向量机在AUC上表现最佳，可解释特征对风险分层和公共卫生筛查至关重要。
摘要：
儿童贫血仍然是尼泊尔公共卫生面临的重大挑战，与生长迟缓、认知障碍和发病率增加有关。使用世界卫生组织血红蛋白阈值，我们为6-59个月龄的儿童定义了贫血状态，并通过将所有贫血严重程度归类为"贫血"和"非贫血"来制定二元分类任务。我们分析了尼泊尔人口普查与健康调查（NDHS 2022）微观数据，包含1,855名儿童，并最初考虑了48个候选特征，涵盖人口统计、经济社会、母体和儿童健康特征。为了获得稳定且有根据的特征集，我们应用了四种特征选择技术（卡方检验、互信息、点双变量相关性和Boruta），并优先选择多方法共识支持的特征。五个特征：儿童年龄、近期发烧、家庭大小、母体贫血和驱虫治疗被所有方法一致选择，而闭经、民族指标和省份经常被保留。然后我们比较了八种传统机器学习分类器（逻辑回归、K近邻、决策树、随机森林、XGBoost、支持向量机、朴素贝叶斯、线性判别分析）与两种深度学习模型（DNN和TabNet），使用标准评估指标，强调F1分数和召回率，因为存在类别不平衡。在所有模型中，逻辑回归获得了最佳召回率（0.701）和最高F1分数（0.649），而DNN获得了最高准确率（0.709），支持向量机产生了最强的区分能力，具有最高AUC（0.736）。总体而言，结果表明，机器学习和深度学习模型都能提供有竞争力的贫血预测，可解释的特征如儿童年龄、感染代理、母体贫血和驱虫史是尼泊尔风险分层和公共卫生筛查中的核心要素。
英文摘要：
Childhood anemia remains a major public health challenge in Nepal and is associated with impaired growth, cognition, and increased morbidity. Using World Health Organization hemoglobin thresholds, we defined anemia status for children aged 6-59 months and formulated a binary classification task by grouping all anemia severities as \emph{anemic} versus \emph{not anemic}. We analyzed Nepal Demographic and Health Survey (NDHS 2022) microdata comprising 1,855 children and initially considered 48 candidate features spanning demographic, socioeconomic, maternal, and child health characteristics. To obtain a stable and substantiated feature set, we applied four features selection techniques (Chi-square, mutual information, point-biserial correlation, and Boruta) and prioritized features supported by multi-method consensus. Five features: child age, recent fever, household size, maternal anemia, and parasite deworming were consistently selected by all methods, while amenorrhea, ethnicity indicators, and provinces were frequently retained. We then compared eight traditional machine learning classifiers (LR, KNN, DT, RF, XGBoost, SVM, NB, LDA) with two deep learning models (DNN and TabNet) using standard evaluation metrics, emphasizing F1-score and recall due to class imbalance. Among all models, logistic regression attained the best recall (0.701) and the highest F1-score (0.649), while DNN achieved the highest accuracy (0.709), and SVM yielded the strongest discrimination with the highest AUC (0.736). Overall, the results indicate that both machine learning and deep learning models can provide competitive anemia prediction and the interpretable features such as child age, infection proxy, maternal anemia, and deworming history are central for risk stratification and public health screening in Nepal.
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