ArticleScientific reports2025
Trade-offs between machine learning and deep learning for mental illness detection on social media.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Explainable AI in Healthcare: A Holistic View of Technical Approaches, Regulatory Frameworks, Reliable Clinical Implementation Insights and Limitations.Diagnostics (Basel, Switzerland) · 2026Review
- Naturalistic Online Language as a Marker of Depression in Midlife and Older Adults: Computational Text Analysis of Bluesky and Reddit Posts.Journal of medical Internet research · 2026Article
- Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis.Journal of medical Internet research · 2026Article
- Self-Reported Health Outcomes in Metabolic Health YouTube Comments: Cross-Sectional Study and Rule-Based Natural Language Processing Framework Development and Validation.Journal of medical Internet research · 2026Observational
- LLM-based annotation and token-augmented modeling for emotional tone classification in online cancer peer-support posts.PLOS digital health · 2026Article
- Use and Performance of Language-based Artificial Intelligence (AI) Models to Screen for Depressive Disorders.Current psychiatry reports · 2026Review
- Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 2026Article
- Domain-informed density extraction for robust mental health classification of long social media posts.Frontiers in artificial intelligence · 2026Article
- Ordinal sentiment classification in cancer support forums: a controlled benchmark of machine learning and transformer models.Frontiers in digital health · 2026Article
- Predicting student mental health through entropy-based features and interpretable cross-attention transformer networks.PloS one · 2026Article
- Multi-modal deep learning model for bipolar depression adolescents with verbal auditory hallucinations.Frontiers in psychiatry · 2026Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- Article
- Beyond text: Using network centralities and AI models to detect suicide risk on Reddit.International journal of clinical and health psychology : IJCHPArticle
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside DL architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints.
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