Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling
2026-06-22 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors studied how to better predict mental health issues in people who use drugs by using a special computer method called machine learning. They combined several techniques to handle tricky data problems, like having too few examples of some mental health conditions and tuning the model for better accuracy. Their model was able to predict different mental health problems well, especially by looking at factors like sleep, physical health, and emotions, rather than demographics. They also made their model easier to understand, which could help doctors trust and use it in real life.
machine learningclass imbalancefeature selectionGenerative Adversarial NetworkXGBoosthyperparameter tuningmental health predictionSHAP explainabilitydrug usersmulticlass classification
Authors
Ahnaf Atef Choudhury, Shahriar Siddique Ayon, Md. Ebrahim Hossain, Abdullah Al Mamun
Abstract
Mental illnesses among drug users are an increasing international issue, particularly in regions where early detection cannot be easily undertaken. The current literature tends to ignore the use of AI-based mental health analysis in drug users, and low quality of the class imbalance treatment, low interpretability, and optimal hyperparameter optimization can lower predictive quality and clinical utility. This study present a detailed, explainable machine learning (ML) model of multiclass mental health prediction, using a multidimensional data set of drug-affected persons. We combine hybrid PCA-Information Gain (PCA-IG) feature selection, Generative Adversarial Network (GAN)-based oversampling, and Dragonfly Algorithm (DA)-optimized XGBoost to address some of the limitations of existing methods. The suggested framework is effective to work with high-dimensional categorical data, address the issue of class imbalance, and improve predictive performance due to intelligent hyperparameter tuning. The experimental findings show that the XGBoost model optimized using the DA, in combination with GAN-based oversampling, has an accuracy of 94.17% and a weighted F1-score of 93.80%, which is better than the traditional and baseline models. The behavioral, lifestyle, and health factors, particularly sleep quality, physical health, and emotional regulation, are strongly predictive of mental health, with demographic factors having little impact, as seen through feature analysis. SHAP-based explainable AI provides easy-to-understand, instance-level information, enhancing interpretability and trust in models to be used in clinical settings. The results indicate that this framework has the potential to generate valid mental health forecasting tools, which would facilitate early intervention and enhance the treatment of drug-influenced people.