Predictive Risk Identification and Resource Optimization in Social Work: A Hybrid Semi-Supervised Learning Framework

Social work practice faces critical challenges in accurately identifying high-risk families for timely intervention due to limited labeled data. This research develops an intelligent system integrating supervised and semi-supervised learning for early warning, coupled with uncertainty-aware resource optimization. We propose a hybrid framework combining Self-training and Co-training leveraging 1,200 labeled and 13,800 unlabeled family cases. The proposed method achieves 85.6% accuracy and 82.4% recall on the high-risk class, with 95% confidence intervals, substantially outperforming supervised-only baselines. A multi-objective allocation model balances effectiveness, cost, and equity while incorporating prediction uncertainty via robust optimization. Sensitivity analysis over 500 bootstrap scenarios demonstrates service quality guarantees. Subgroup fairness audits show consistent recall across demographic groups (max gap 1.2%). The optimized allocation reduces manpower by 22% while improving intervention success from 68.5% to 84.2%. SHAP-based explainability and human oversight support ethical deployment in social services.