用GAN生成数据缓解自杀预测中的极端不平衡问题
Handling Extreme Class Imbalance: Using GANs in Data Augmentation for Suicide Prediction
- 用GAN生成合成数据扩充稀缺的自杀企图样本
- 逻辑回归在真实测试集上达到0.91的加权F1分数
- 适合做精神健康预警系统开发的研究者参考
自杀预测对预防至关重要，但真实数据中正样本极少，导致极端类别不平衡。本文采用机器学习（ML）构建模型，并运用生成对抗网络（GAN）等深度学习技术生成合成数据以增强数据集。初始数据集共656个样本，其中仅有4个正样本，亟需数据增强。实验对比了多种机器学习模型，包括可解释性模型与黑箱算法模型。在真实测试数据上，逻辑回归（LR）实现加权精确率0.99、加权召回率0.85、加权F1得分0.91；随机森林（RF）分别为0.98、0.99、0.99；支持向量机（SVM）为0.99、0.76、0.86。LR和SVM均正确识别1例自杀未遂（敏感性1.0），但分别误判20和31例非企图者为企图者（特异性0.85与0.76）；而随机森林未能识别任何企图案例（敏感性0.0），但无误报（特异性1.0）。结果表明，模型整体有效，且GAN在支持自杀预防建模中发挥了关键作用。
原文摘要 · Abstract (English)
Suicide prediction is the key for prevention, but real data with sufficient positive samples is rare and causes extreme class imbalance. We utilized machine learning (ML) to build the model and deep learning (DL) techniques, like Generative Adversarial Networks (GAN), to generate synthetic data samples to enhance the dataset. The initial dataset contained 656 samples, with only four positive cases, prompting the need for data augmentation. A variety of machine learning models, ranging from interpretable data models to black box algorithmic models, were used. On real test data, Logistic Regression (LR) achieved a weighted precision of 0.99, a weighted recall of 0.85, and a weighted F1 score of 0.91; Random Forest (RF) showed 0.98, 0.99, and 0.99, respectively; and Support Vector Machine (SVM) achieved 0.99, 0.76, and 0.86. LR and SVM correctly identified one suicide attempt case (sensitivity:1.0) and misclassified LR(20) and SVM (31) non-attempts as attempts (specificity: 0.85 & 0.76, respectively). RF identified 0 suicide attempt cases (sensitivity: 0.0) with 0 false positives (specificity: 1.0). These results highlight the models' effectiveness, with GAN playing a key role in generating synthetic data to support suicide prevention modeling efforts.
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