Machine Learning Model to Predict Autism Spectrum Disorder Using Eye Gaze Tracking
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is caused by a group of complex neurological disorders which inhibit the development of a person’s social, behavioral and verbal skills. It is of paramount importance for early diagnosis of this disorder as studies have shown that starting the ASD treatment in the early stages of a patient’s life can provide fruitful results; treatment plans are better able to work when the child is still at his developing age. However, the current treatment is long and requires professionals, so many children are not diagnosed with ASD in their early childhood. In this paper, we propose a robust framework for detecting Autism Spectrum Disorder (ASD) in patients using their tracked eye-gaze as features. Our approach consists of using supervised learning models to predict children with ASD. The result shows the ability to classify patients with reasonable accuracy. This approach can assist doctors because it only requires five minutes of video with a camera rather than several hours of behavioral and cognitive tests conducted by a licensed professional.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".