Machine Learning-Based Prediction of Childhood Stunting in Bangladesh: Fairness and Temporal Robustness Assessment

📄 arXiv: 2609.24386v1 📥 PDF

作者: Md Ahshanul Haque, Muhammad Ashad Kabir

分类: cs.LG

发布日期: 2026-09-21

备注: Accepted to AusDM, 15 pages

💡 一句话要点

基于机器学习的孟加拉国儿童生长迟缓预测模型

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 儿童生长迟缓 机器学习 公共卫生 时间稳健性 子群体公平性 特征选择 模型评估

📋 核心要点

  1. 儿童生长迟缓在孟加拉国仍然是一个严重的公共卫生问题，现有预测方法缺乏时间稳健性和公平性评估。
  2. 本研究利用机器学习技术，基于国家健康调查数据，开发了儿童生长迟缓的预测模型，并进行了公平性和时间稳健性评估。
  3. 实验结果表明，TabPFN模型在不同时间段的预测中表现优异，强调了时间验证和子群体公平性的重要性。

📝 摘要（中文）

儿童生长迟缓是孟加拉国的重大公共卫生问题，受多种因素影响。该研究利用2007至2022年的全国性人口健康调查数据，开发了机器学习模型以预测儿童生长迟缓，并评估了模型的时间稳健性和子群体公平性。研究涵盖了0至59个月的儿童，最终分析样本包括18844名儿童，其中35.05%存在生长迟缓。研究结果显示，TabPFN模型在开发测试集中表现最佳，平衡准确率为67.58%。

🔬 方法详解

问题定义：本研究旨在解决儿童生长迟缓的预测问题，现有方法在时间变化和子群体公平性方面存在不足。

核心思路：通过使用机器学习模型，结合全国性健康调查数据，评估不同时间段和子群体的生长迟缓预测能力，以提高模型的适用性和公平性。

技术框架：研究首先进行特征选择，使用KNN置换重要性选择预测变量，然后评估十种传统机器学习算法和一个预训练模型TabPFN的性能。模型开发使用2007、2011和2014年的数据，2018和2022年的数据用于时间测试。

关键创新：本研究的创新点在于综合考虑时间稳健性和子群体公平性，提出了一种新的评估框架，以确保模型在不同时间和人群中的有效性。

关键设计：研究中使用了平衡准确率、AUROC、F1-score等多种性能指标，确保模型评估的全面性。特征选择方法和模型选择过程经过严格验证，以确保最终模型的可靠性。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，TabPFN模型在开发测试集中达到了67.58%的平衡准确率，优于其他传统模型。时间测试中，Gradient Boosting和XGBoost在不同年份的表现也显示出良好的稳健性，强调了时间验证的重要性。

🎯 应用场景

该研究的成果可广泛应用于公共卫生领域，尤其是在儿童健康监测和干预策略的制定中。通过准确预测儿童生长迟缓，能够帮助政策制定者和卫生工作者更好地分配资源，制定针对性的干预措施，从而改善儿童的健康状况。

📄 摘要（原文）

Childhood stunting remains a major public health concern in Bangladesh and reflects long-term growth failure influenced by child, maternal, household, socioeconomic, and health-service factors. This study used nationally representative Bangladesh Demographic and Health Survey data from 2007 to 2022 to develop machine learning models for population-level prediction of childhood stunting and to assess temporal robustness and subgroup fairness. Children aged 0-59 months with complete anthropometric and predictor data were included. Data from the 2007, 2011, and 2014 survey rounds were used for model development, while the 2018 and 2022 rounds were retained as temporal test datasets. Twelve feature-selection approaches were assessed, and the KNN permutation importance-selected predictor set was used for final model evaluation. Eleven machine learning models were evaluated: ten conventional algorithms and one pretrained tabular foundation model, TabPFN. Performance was assessed using balanced accuracy, AUROC, F1-score, Brier score, and expected calibration error. Subgroup fairness was examined by child sex, place of residence, and socioeconomic status. The final analytic sample included 18,844 children, of whom 35.05% were stunted. In the development hold-out test dataset, TabPFN showed the highest observed balanced accuracy overall at 67.58%, while AdaBoost showed the highest observed balanced accuracy among conventional models at 67.51%. In temporal testing, the highest observed balanced accuracy was found for Gradient Boosting in BDHS 2018 and XGBoost in BDHS 2022. Model performance varied across survey rounds and subgroups, highlighting the importance of temporal validation, subgroup fairness assessment, and transparent interpretation in public health prediction modeling.