用机器学习精准识别乌拉圭援助项目潜在参与者
Machine Learning for Identifying Potential Participants in Uruguayan Social Programs
- 基于1.5万份历史数据，训练多种机器学习模型预测家庭是否符合条件
- 通过样本平衡与阈值优化，模型准确率显著提升
- 适合政策评估、公共管理与算法公平性研究者参考
本研究探索利用机器学习优化乌拉圭'育婴支持计划'（PAF）的家庭筛选流程。分析了包含15,436个先前推荐案例的匿名数据库，重点关注孕产妇及四岁以下儿童家庭。目标是开发一个预测算法，判断家庭是否符合项目准入条件。该模型旨在简化评估流程，提升资源分配效率，使团队有更多时间开展直接帮扶。研究进行了全面数据分析，并实施了神经网络（NN）、XGBoost（XGB）、LSTM及集成模型等多类机器学习方法。针对类别不平衡问题，采用SMOTE和RUS技术，并优化决策阈值以提升预测精度与平衡性。结果表明，这些技术在高效分类需援助家庭方面具有显著潜力。
原文摘要 · Abstract (English)
This research project explores the optimization of the family selection process for participation in Uruguay's Crece Contigo Family Support Program (PAF) through machine learning. An anonymized database of 15,436 previous referral cases was analyzed, focusing on pregnant women and children under four years of age. The main objective was to develop a predictive algorithm capable of determining whether a family meets the conditions for acceptance into the program. The implementation of this model seeks to streamline the evaluation process and allow for more efficient resource allocation, allocating more team time to direct support. The study included an exhaustive data analysis and the implementation of various machine learning models, including Neural Networks (NN), XGBoost (XGB), LSTM, and ensemble models. Techniques to address class imbalance, such as SMOTE and RUS, were applied, as well as decision threshold optimization to improve prediction accuracy and balance. The results demonstrate the potential of these techniques for efficient classification of families requiring assistance.
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