Identification of depression predictors from standard health surveys using machine learning
Bibliographic record
Abstract
Depression has profound personal, societal, and economic impacts. Leveraging advances in technology can help identify predictors of depression. In this study, we compared seven machine learning (ML) algorithms to identify depression predictors using publicly available datasets from standard health surveys. We obtained data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020, including medical, mental, demographic, and lifestyle information from 8965 individuals aged 18 to 80 years. Our study identified strongly correlated features of depression and demonstrated that ML algorithms can accurately identify depression predictors. The performance of the algorithms was evaluated using standard metrics. Among the algorithms tested, the Neural Network algorithm showed the highest overall performance, with an area under the curve of 91.34%, which significantly outperformed results obtained with traditional statistical methods such as logistic regression and nomograms. This study demonstrates how ML can mine standard health surveys and identify depression predictors in a more accurate and nuanced fashion than other approaches. The findings of this study further suggest that incorporating heterogeneous data can enhance the performance of ML algorithms. These algorithms can be a valuable complementary tool for clinicians, particularly in remote settings, facilitating data analysis, and accelerating knowledge discovery in mental health studies.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.000 |
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 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".