Regularized CNN Based Model for Analyzing, Predicting Depression and Handling Overfitting
Bibliographic record
Abstract
Depression is serious and common disorder among human being that affects mental health.Large scale research is carried out to identify the risk of depression.Using technology to identify individuals with depression could connect patients with the help they need more quickly and easily while reducing healthcare costs and burden on physicians.In this paper Convolution Neural Network (CNN) is implemented using Patient Health Questionnaire (PHQ-9) screened NHANES (National Health and Nutrition Examination Survey) dataset from 1999 to 2014.The proposed framework for automatic depression prediction shows accuracy starting from 92.37 to 1.0.Continuing the research, dropout layer is used in visible and hidden layers to avoid overfitting.After the use of dropout layer the training and validation accuracy is synchronized and validation loss is less than training loss.Hence generalized model is obtained by using regularized CNN with 100% accuracy.When the implemented model is compared with existing work in the same area then it is observed that till date using CNN this is the first attempt to achieve 100% accuracy on NHANES dataset for identifying the risk of depression using PHQ-9 questionnaire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".