Classification models combined with optimized features for mental stress prediction
Bibliographic record
Abstract
Mental stress is a growing global health concern, closely linked to psychological, behavioral, and physiological disorders. Accurate and early prediction of mental stress is crucial for timely interventions and improved health outcomes. Despite numerous studies leveraging machine learning (ML) techniques for stress classification, many have overlooked the integration of systematic feature selection and comprehensive model evaluation, limiting generalizability and interpretability. To address these gaps, this study proposes a robust ML-based framework that combines optimized feature selection methods - Recursive Feature Elimination (RFE), Extra Trees (ET), and Boruta - with various classification algorithms including Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting, and voting classifier. The models were evaluated using 10-fold cross-validation and ranked using the TOPSIS multi-criteria decision-making approach. The experimental results demonstrate high predictive performance across models (accuracy ≥ 0.98), with RF, DT, MLP, and Gradient Boosting achieving perfect accuracy (1.00). Among all configurations, the RF-Boruta model emerged as the most optimal (TOPSIS score: 0.914558). These findings highlight the effectiveness of combining systematic feature optimization with ML classification for accurate and interpretable stress prediction, offering valuable insights for data-driven mental health interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".