Yaşam Tarzı ve Teknoloji Kullanımının Ruh Sağlığı Üzerindeki Etkilerinin Derin Öğrenme Yöntemleri ile Sınıflandırılması
Bibliographic record
Abstract
This study aims to classify individuals’ mental health status by analyzing the impact of lifestyle and technology usage habits through deep learning methods. The dataset includes digital behavior and lifestyle indicators from 1,000 participants across Germany, Australia, India, the USA, Canada, and the UK. Key variables include screen time, social media usage, sleep duration and quality, stress level, physical activity, and productivity. After preprocessing—filling missing values with median, encoding categorical variables, and standardizing features—the data were split into 80% training and 20% testing sets. Four classification models were applied: Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Autoencoder+MLP, and Decision Tree+MLP. Models were evaluated using accuracy, precision, recall, F1-score, and ROC curves. Findings revealed that the Decision Tree+MLP hybrid model achieved the highest performance with 90% accuracy, 92% precision, 85% recall, and 88% F1-score. While other models struggled to distinguish the “Chronic” mental health class, this hybrid model provided balanced and effective classification across all categories. In conclusion, digital behavior patterns and lifestyle indicators are significant predictors of mental health status, and AI-based models can uncover these relationships with high accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".