Data Mining–Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach
Uloženo v:
| Vydáno v: | JMIR Formative Research vol. 9 (2025), p. e72115-e72131 |
|---|---|
| Hlavní autor: | |
| Další autoři: | , |
| Vydáno: |
JMIR Publications
|
| Témata: | |
| On-line přístup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tagy: |
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3241585063 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2561-326X | ||
| 024 | 7 | |a 10.2196/72115 |2 doi | |
| 035 | |a 3241585063 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Eldawansy, Mohamed | |
| 245 | 1 | |a Data Mining–Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach | |
| 260 | |b JMIR Publications |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Machine learning | ||
| 653 | |a Behavior | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Social interaction | ||
| 653 | |a Data mining | ||
| 653 | |a Quantitative psychology | ||
| 653 | |a Intervention | ||
| 653 | |a Neural networks | ||
| 653 | |a Questionnaires | ||
| 653 | |a Autistic children | ||
| 653 | |a Integrated approach | ||
| 653 | |a Automation | ||
| 653 | |a Facial recognition technology | ||
| 653 | |a Emotions | ||
| 653 | |a Social anxiety | ||
| 700 | 1 | |a Hazem El Bakry | |
| 700 | 1 | |a Shohieb, Samaa M | |
| 773 | 0 | |t JMIR Formative Research |g vol. 9 (2025), p. e72115-e72131 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3241585063/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3241585063/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3241585063/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |