Predictive Models for Early Identification of Autism Spectrum Disorder using ML & DL techniques
Bibliographic record
Abstract
Early identification of autism spectrum disorder (ASD) is essential for early therapies that improve effects. Conventional diagnostic methods can be slow and costly, leading to delays in necessary treatment. In this work, we introduce a unique method to improve the diagnostic accuracy of ASD, especially in its early phases, by combining machine learning and deep learning approaches.By leveraging algorithms such as Support Vector Machines (SVM), Random Forest Classifier (RFC), Logistic Regression (LR), and K-Nearest Neighbors (KNN), [11] our goal is to streamline the diagnostic process while maintaining high levels of reliability and accuracy. [1]Our method involves integrating these algorithms with a comprehensive dataset in order to create a prediction model that can correctly diagnose ASD. Furthermore, we introduce an innovative technique that utilizes a pre-trained convolutional neural network (CNN) for feature extraction and binary classification based on facial images [4]. By examining the human face as a physiological indicator, our approach aims to detect subtle neurological markers of ASD, thereby speeding up the diagnostic process. Combining the predictive capabilities of machine learning with the image analysis strengths of deep learning, we provide a framework for identifying ASD susceptibility in children. This pioneering approach has the potential to revolutionize ASD diagnosis, offering quicker and more cost-effective solutions for clinicians and caregivers, ultimately improving impacts for people with ASD.
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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 | 0.006 |
| 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) | 0.005 | 0.002 |
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".