Data Mining–Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach
Bibliographic record
Abstract
BACKGROUND: Gelotophobia, the fear of being laughed at, is a social anxiety condition that affects approximately 6% of neurotypical individuals and up to 45% of those with autism spectrum disorder (ASD). This comorbidity can significantly impair the quality of life, particularly in adolescents with high-functioning ASD, where the prevalence reaches 41.98%. Accurate and automated detection tools could enhance early diagnosis and intervention. OBJECTIVE: This study aimed to develop a deep learning-based diagnostic system that integrates facial emotion recognition with validated questionnaires to detect gelotophobia in individuals with or without ASD. METHODS: The system was trained to identify ASD status using a balanced dataset of 2932 facial images (n=1466; 50% from individuals with ASD and n=1466; 50% from neurotypical individuals). The images were processed using the DeepFace library to extract facial features, which were then used as input for the deep learning classifier. After identifying ASD status, the same images were further analyzed using the pretrained DeepFace model to evaluate facial expressions for signs of gelotophobia. In cases where facial cues were ambiguous, the GELOPH<15> questionnaire, consisting of 15 items, was administered to confirm the diagnosis The system was fully implemented using the Python programming language. Deep learning models were developed using libraries such as PyTorch for training the multilayer perceptron classifier, while CUDA was used to accelerate computations on compatible graphics processing units. Additional libraries from the Python programming language, such as scikit-learn, NumPy, and Pandas, were used for preprocessing, model evaluation, and data manipulation. DeepFace was integrated using its Python application programming interface for facial recognition and emotion classification. RESULTS: The dataset comprised 2932 facial images collected from platforms such as Kaggle and ASD-related websites, including 1466 (50%) images of children with ASD and 1466 (50%) images of neurotypical children. The dataset was split into 2653 (90.48%) training samples and 279 (9.51%) testing samples, with each image contributing 100,352 extracted features. We applied various machine learning models for ASD identification. The system achieved an overall prediction accuracy of 92% across both training and testing datasets, with the multilayer perceptron model demonstrating the highest testing accuracy. The system successfully classified gelotophobia in cases where facial expressions were clear. However, in cases of ambiguous facial cues, the DeepFace model alone was insufficient. Incorporating the GELOPH<15> questionnaire improved diagnostic reliability and consistency. CONCLUSIONS: This study demonstrates the effectiveness of combining deep learning techniques with validated diagnostic tools for detecting gelotophobia, particularly in individuals with ASD. The high accuracy achieved highlights the system's potential for clinical and research applications, contributing to the improved understanding and management of gelotophobia among groups considered socially vulnerable. Future research could expand the system's applications to broader psychological assessments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".