An Integrated Hybrid Artificial Intelligence Framework For Early Autism Spectrum Disorder Screening Using Random Forest Classification And Resnet18 Transfer Learning
Aug 2026· Adolescência e Saúde· 0 citations· 21 references
TL;DR
Experimental results demonstrate that the Random Forest model achieved an accuracy of 96.8%, while the ResNet18 model attained 94.2% accuracy, indicating the effectiveness of combining behavioral and facial information for preliminary ASD screening.
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent deficits in social communication, interaction, and behavioral patterns. Early screening is essential for timely intervention; however, conventional diagnostic procedures are often resource-intensive, subjective, and dependent on clinical expertise. This study proposes an integrated hybrid artificial intelligence framework for preliminary ASD screening by combining machine learning and deep learning techniques using structured behavioral data and facial image analysis. The first module employs a Random Forest classifier to analyze Autism Quotient-10 (AQ-10) questionnaire responses together with demographic and medical history attributes, including age, gender, ethnicity, family history, and developmental factors. The second module utilizes ResNet18 with transfer learning to classify facial images after preprocessing and data augmentation, enabling non-invasive image-based screening. Both predictive models are integrated into a user-friendly screening application developed using CustomTkinter, allowing independent questionnaire-based and image-based assessments through a unified graphical interface. Model training incorporates feature preprocessing, hyperparameter optimization using Grid Search with cross-validation, and performance evaluation using accuracy, precision, recall, F1-score, specificity, and area under the receiver operating characteristic curve (ROC-AUC). Experimental results demonstrate that the Random Forest model achieved an accuracy of 96.8%, while the ResNet18 model attained 94.2% accuracy, indicating the effectiveness of combining behavioral and facial information for preliminary ASD screening. The proposed framework is intended as a clinical decision-support tool to facilitate accessible and timely screening and is not designed to replace comprehensive clinical diagnosis.
A novel approach with an Optimized Pre-Trained Feature Selection With Support Vector Machine (OPFSVM) detection model for ASD is proposed to overcome the existing challenges and problems and highlight the implemented method's high effectiveness in early ASD detection and position it as an effective tool for timely and rapid analysis.
Robin Khurana, Satyaveer Singh· international journal of eng...· 0 citations
These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool and will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.
Aina Khairina Ahmad Khair, Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad et al.· Bulletin of Electrical Engin...· 0 citations
A comprehensive machine learning framework to classify ASD severity (mild, moderate, severe) is developed and validates by investigating the differential impact of feature engineering and selection, revealing a critical “evaluation paradox” where radical, unguided feature reduction improved geometric cluster cohesion but degraded clinical accuracy.
Arazo Mohammed Mustafa· ARID International Journal f...· 0 citations
A Hybrid Intelligent Model designed to predict ASD in pediatric cases, leveraging adaptive neuro-fuzzy systems integrates artificial neural network capabilities with fuzzy logic, offering a comprehensive approach to ASD prediction.
N. Okafor, C. Ituma, R. Nweze· Communication in Physical Sc...· 0 citations
Autism Spectrum Disorder (ASD) classification using machine learning has shown promising results on behavioral screening datasets, however, such performance may be influenced by embedded questionnaire scoring and threshold-based patterns. This study presents an explainable multi-model ASD classification Framework that consist of questionnaire based screening, augmentation analysis, rule based interpretation, explainable artificial intelligence, and rs-fMRI based neuroimaging classification. The proposed framework is organized into three progressive models. ASD classification on child behavioral dataset focuses on child behavioral screening data and evaluated Logistic Regression, XGBoost, Linear Support Vector Machine, and Bernoulli Naive Bayes with XAI and rule-based interpretation. Multi-cohort ASD classification framework combined child, adolescent, and and adult AQ_10 based datasets and assessed the same classifiers under real, SMOTE, ADASYN, CTGAN, and TVAE training conditions. ABIDE-1 multi-modal ASD classification extended the analysis to ABIDE-1 rs-fMRI functional connectivity and phenotypic features using ElasticNet Logistic Regression, Linear SVM, Ridge Classifier, and SGD LogLoss classifier. The questionnaire based models achieved strong performance, with ASD classification on child behavioral dataset framework obtaining a best accuracy of 97% using Linear SVM and Multi-cohort ASD classification framework reaching up to 100% accuracy in selected real and traditional oversampling settings for logistic Regression and linear SVM. SMOTE and ADASYN produced more stable performance than CTGAN and TVAE. ABIDE-1 multi-modal ASD classification framework achieved a best accuracy of 70% using ElasticNet Logistic Regression, reflecting the grater complexity of neuroimaging based ASD classification.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder which necessitates early and precise diagnosis to facilitate prompt intervention and enhance long-term results. Using behavioural and demographic data, this research offers a machine learningbased method for identifying ASD. The proposed system incorporates comprehensive data preprocessing techniques, such as addressing missing values, encoding categorical features, and feature scaling, to increase model efficiency. Additionally, the most pertinent characteristics that contribute to the classification of ASD are found using feature selection techniques. The predictive power of four machine learning (ML) algorithms: Support Vector Machine (SVM), Random Forest (RF), Cat-Boost (CB), and Light Gradient Boosting Machine (LGBM) is assessed through implementation. Following training and validation using appropriate data partitioning methodologies, the model's performance is evaluated using different performance metrics. A comparative study is undertaken to find out how well every model represents complex trends in the dataset. According to the experimental results, boosting-based methods in particular, Cat-Boost and LGBM perform better because they can effectively handle categorical variables and minimise overfitting. Cat-Boost achieved best results with an accuracy of 85.14%. This study shows how cutting-edge machine learning methods can improve ASD screening and assist with data-driven clinical decision-making.
