SAGE：用于可解释产后抑郁症风险预测的稳定性感知图集成特征选择方法
SAGE: Stability-Aware Graph-Based Ensemble Feature Selection for Explainable Postpartum Depression Risk Prediction
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中文总结 AI 辅助
针对产后抑郁症预测的可解释性、稳定性与类别不平衡问题，研究提出SAGE系统，结合局部XAI与GA-ANN，在766人队列中仅用16个特征实现87.96%准确率，性能优于基线方法，可作为医疗资源有限场景的PPD早期识别工具。
中文摘要 AI 辅助
产后抑郁症（PPD）对母婴健康造成重大负担，尤其在中低收入国家，其患病率超过19%。尽管机器学习在PPD预测方面取得了进展，但现有方法存在患者层面缺乏临床实用性的全局解释不透明、特征选择不稳定、类别不平衡下泛化能力差等局限。本文提出SAGE，即稳定性感知图集成特征选择系统，该系统结合了局部可解释人工智能（XAI）与遗传算法优化的人工神经网络（GA-ANN）。研究使用包含766名产后女性的初级队列，SAGE将信息论相关性、基于主成分分析（PCA）的结构、图交互与自助法稳定性加权相结合，以识别稳健且非冗余的预测因子。经遗传算法优化并辅以生成对抗网络（GAN）过采样的GA-ANN架构，仅用16个特征便实现了87.96%的准确率、86.32%的F1分数和0.88的AUC，性能优于基线及其他特征选择方法。EPDS评分、PHQ-9评分、母职感受、虐待史等心理与社会经济因素是主要预测因子，人口统计学因素影响较小。基于局部可解释模型-不可知解释（LIME）的解释可提供图中所选特征的实例级洞察，支持个性化风险评估。研究表明SAGE是一款可扩展、可解释且适用于医疗资源有限场景的PPD早期识别临床工具。
英文摘要
Postpartum depression (PPD) poses a major burden on maternal and child health, especially in low- and middle-income countries where prevalence exceeds 19%. Despite advancements in machine learning for PPD prediction, current approaches are limited by opaque global explanations that lack clinical usefulness at the patient level, unstable feature selection, and poor generalization under class imbalance. We propose SAGE, a Stability-Aware Graph-Based Ensemble feature selection system that incorporates both local explainable AI and a genetically optimized artificial neural network (GA-ANN). Using a primary cohort of 766 postpartum women, SAGE combines information-theoretic relevance, PCA-based structure, and graph-based interactions with bootstrap stability weighting to identify robust and non-redundant predictors. The GA-ANN architecture, optimized using a genetic algorithm and enhanced with GAN based oversampling, achieved strong performance with 87.96% accuracy, 86.32% F1 score, and 0.88 AUC using only 16 features, outperforming baseline and other feature selection methods. Psychological and socioeconomic factors such as EPDS score, PHQ-9 score, feelings about motherhood, and abuse history are the main predictors, while demographic factors have less influence. The LIME-based explanations allow instance-based insight into selected features from the graph, enabling personalized risk assessment. The findings make SAGE a scalable, interpretable, and clinical tool for early identification of PPD in health-care limited resources.
发表机构
- Brunel University London（伦敦布鲁内尔大学）
- Technical University of Darmstadt（达姆施塔特工业大学）
- American International University-Bangladesh（孟加拉国国际大学）
- Dhaka University of Engineering and Technology（达卡工程技术大学）
- George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。
