Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling

• URL: http://arxiv.org/abs/2606.22780v1
• Date: Mon, 22 Jun 2026 02:42:58 GMT
• Title: Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling
• Abstract summary: This study present a detailed, explainable machine learning (ML) model of mental health prediction using a multidimensional data set of drug-affected persons.<n>The behavioral, lifestyle, and health factors, particularly sleep quality, physical health, and emotional regulation, are strongly predictive of mental health.<n>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.
• Score: 0.526647163965749
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SAGE: Stability-Aware Graph-Based Ensemble Feature Selection for Explainable Postpartum Depression Risk Prediction [0.4908879620102718]
  Postpartum depression (PPD) poses a major burden on maternal and child health, especially in low- and middle-income countries.<n>We propose SAGE, a Stability-Aware Graph-Based Ensemble feature selection system that incorporates both local explainable AI and a genetically artificial neural network (GA-ANN)<n>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.
  arXiv  Detail & Related papers  (2026-08-24T05:11:42Z)
• A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction [14.398540855932708]
  We focus on diagnosis-related features and compare five feature selection paradigms for opioid use disorder (OUD) prediction.<n>We use a unified preprocessing and evaluation framework and assess each method by downstream predictive performance, resampling stability, and representation of infrequent diagnosis codes.<n>Our results demonstrate that performance improves with larger feature budgets with diminishing returns beyond a moderate size.
  arXiv  Detail & Related papers  (2026-08-04T19:37:29Z)
• Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules [10.26837045598536]
  Deep learning and tree-based ensemble methods can achieve high accuracy, but their black-box nature remains a major obstacle to clinical deployment.<n>We propose Medical Heuristic Learning (MHL), an instantiation of the learning-beyond-gradients paradigm.<n>MHL uses a large language model (LLM)-driven workflow that integrates statistical probes, medical knowledge probes, rule synthesis, and code-level iterative refinement to optimize a deterministic and executable decision system.
  arXiv  Detail & Related papers  (2026-06-15T07:43:06Z)
• Towards a General Intelligence and Interface for Wearable Health Data [45.854819784803055]
  We propose a foundation model for wearable health that is pretrained on more than one trillion minutes of unlabeled sensor signals.<n>We find that this population scale representation unlocks label-efficient few-shot learning and generative capabilities for robust daily metric estimation.
  arXiv  Detail & Related papers  (2026-05-21T17:24:06Z)
• Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes [55.310195121276074]
  We propose a Knowledge graph-enhanced, Prototype-aware, and Interpretable (KPI) framework to predict diseases.<n>It integrates structured and trusted medical knowledge into a unified disease knowledge graph, constructs clinically meaningful disease prototypes, and employs contrastive learning to enhance predictive accuracy.<n>It provides clinically valid explanations that closely align with patient narratives, highlighting its practical value for patient-centered healthcare delivery.
  arXiv  Detail & Related papers  (2025-12-09T05:37:54Z)
• Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media [0.16385815610837165]
  This study provides a comprehensive evaluation of state-of-the-art transformer models against Long Short-Term Memory (LSTM) based approaches.<n>We construct a large annotated dataset using different text embedding techniques for mental health disorder classification on Reddit.<n> Experimental results demonstrate the superior performance of transformer models over traditional deep-learning approaches.
  arXiv  Detail & Related papers  (2025-07-17T04:58:31Z)
• Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction [40.96453902709292]
  We propose pretraining strategies that leverage a phenomenon in real-world applications.<n>In medicine, components of the same biological signaling pathways frequently influence both baseline risk and treatment response.<n>We use this structure to incorporate side information and develop models that can exploit synergies between risk prediction and causal effect estimation.
  arXiv  Detail & Related papers  (2025-05-01T05:12:14Z)
• Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions [0.5249805590164902]
  We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders.<n>We use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data.<n>We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy.
  arXiv  Detail & Related papers  (2025-03-03T11:48:01Z)
• Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques [0.0]
  Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. We implemented six machine learning models using the MIMIC-III database.
  arXiv  Detail & Related papers  (2024-08-02T09:44:18Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.