Machine Learning for Early Detection of Child Depression: A Data-Driven Approach
Bibliographic record
Abstract
Children face trauma due to numerous factors such as parental fights, bullied in school, being below average student, racist comments, physical abuse etc. This trauma deteriorates the child's mental health and may lead to depression as well. Medical field is immensely dedicated to combat the depression among children and one of such approaches adapted includes the use Machine Learning models to predict child depression. This research work is focusing on seven different machine learning algorithms, including SVM Poly, K-Nearest Neighbor, SVM RBF, SVM Linear, Decision Tree, Gradient Boosting, Random Forest and Logistic Regression. This research examines the performance of these algorithms in detecting depressive symptoms in a child and adolescent population. In accordance with the outcomes of the research, SVM Linear algorithm emerged as the most accurate method, with an excellent accuracy rate of 95.54%. This algorithm demonstrated higher sensitivity in detecting depression symptoms in children and teens opening the possibility to prompt intervention and assistance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".