ArticleScientific reports2025
Artificial intelligence for predicting depression anxiety and stress using psychometric data.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence in the Assessment of Heart Rate Variability as an Instrument to Understand the Connection Between Psychologic and Psychiatric Conditions and the Heart.Bioengineering (Basel, Switzerland) · 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
- Platform shift in mental health support among undergraduates: from campus counselors to GenAI-based consultation.Frontiers in psychiatry · 2026Article
- A gender-emotion interaction multi-task network for depression recognition via transformer-based multimodal fusion.Frontiers in psychiatry · 2026Article
- Agitation, Alzheimer's disease, and autophagy: mechanistic insights into aging pathways, gut microbiome, and artificial intelligence.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mental health is a crucial aspect of overall well-being, yet it is often overlooked due to stigma and limited accessibility to care. This study investigates the ability of artificial intelligence (AI) to predict common psychological conditions, depression, anxiety, and stress, using validated psychometric data. We analyzed responses from the Depression Anxiety Stress Scales-42 (DASS-42) questionnaire, combined with demographic information, drawn from a large publicly available dataset of 39, 775 anonymized participants. Five machine learning models were evaluated: decision tree, random forest, k-nearest neighbor, naive Bayes, and support vector machine (SVM). Data preprocessing included handling missing values, demographic standardization, and validity checks. Model performance was assessed using stratified train-test splits and five-fold cross-validation. The SVM model achieved the highest accuracy (99.3% for depression, 98.9% for anxiety, 98.8% for stress). These findings highlight the potential of AI-based approaches for early mental health screening, although further clinical validation is necessary to ensure their real-world applicability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.