Impact of Subject-specific Training Data in Anxiety Level Classification from Physiologic Data
Bibliographic record
Abstract
The autonomic nervous system is known for the fight or flight response. Anxiety affects the autonomic nervous system, causing heightened heart rate and electrodermal activity. This paper explores machine learning methods to predict two- and three-level anxiety in spider fearful individuals watching spider video clips in a controlled trial. Features are extracted from electrocardiogram and electrodermal time-series signals. Specifically, this paper explores the performance of such models as the amount of data pertaining to the test subject increases in the training set. Standard K-fold cross-validation is here compared to leaky group-fold cross-validation with sample imputation, where we systematically vary the the number of samples from the test subject that are included in the training set. While it is possible to reach 78% and 60% k-fold accuracy for a two- and three-level anxiety prediction, respectively, excluding all test subject data from the training set causes the accuracy to drop to 73% and 45%. The results demonstrate that the features and models used here do not generalize for inter-subject classification tasks and that care should be taken when splitting subject data between training and test data. Furthermore, our results address the "cold start problem" by providing an indication of how much data would be required from a new subject before accurate prediction of anxiety is possible from physiologic data.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".