Handling Extreme Class Imbalance: Using GANs in Data Augmentation for Suicide Prediction

2025-10-20

AI Summary

To address extreme class imbalance caused by severe scarcity of positive samples in suicide prediction, this paper proposes a generative adversarial network (GAN)-based data augmentation method to synthesize high-fidelity positive instances and mitigate data sparsity. Unlike conventional oversampling techniques, GAN-based augmentation better preserves the underlying high-dimensional feature distribution. Evaluated on real-world clinical text data, the approach is integrated with logistic regression, random forest (RF), and support vector machine (SVM) classifiers. Results show that RF achieves 0.98 weighted precision, 0.99 weighted recall, and 0.99 weighted F1-score after GAN augmentation—significantly outperforming all baselines. Ablation studies confirm that the synthetic samples critically enhance generalization under limited positive-label conditions. This work establishes a reproducible and scalable generative data augmentation paradigm for predicting rare, high-risk events.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Ensemble Methods
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Addressing extreme class imbalance in suicide prediction datasets
• Using GANs to generate synthetic data for augmentation
• Improving machine learning model performance for rare positive cases

Innovation

• Using GANs to generate synthetic data samples
• Applying machine learning models for suicide prediction
• Enhancing dataset to handle extreme class imbalance