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.

翻译：本研究项目探索通过机器学习优化乌拉圭"伴你成长"家庭支持计划（PAF）的家庭遴选流程。研究分析了包含15,436个历史转介案例的匿名数据库，重点关注孕妇及四岁以下儿童群体。主要目标是开发一种能够预测家庭是否符合项目准入条件的算法模型。该模型的实施旨在简化评估流程，实现更高效的资源分配，使团队能将更多时间投入直接支持服务。研究涵盖详尽的数据分析及多种机器学习模型的实现，包括神经网络（NN）、XGBoost（XGB）、长短期记忆网络（LSTM）以及集成模型。针对类别不平衡问题，采用了SMOTE和RUS等技术，并通过决策阈值优化来提升预测精度与平衡性。研究结果证实了这些技术在高效分类需要援助家庭方面的应用潜力。