2026· Communication in Physical Sciences· 0 citations
TL;DR
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.
Abstract
Autism Spectrum Disorder (ASD) presents a significant challenge in early diagnosis and intervention due to its complexand varied symptomatology. ASD poses significant challenges in early detection and intervention due to its multifaceted nature. This study presents a Hybrid Intelligent Model designed to predict ASD in pediatric cases, leveraging adaptive neuro-fuzzy systems. The model integrates artificial neural network capabilities with fuzzy logic, offering acomprehensive approach to ASD prediction. A diverse dataset comprising behavioral observations, developmental milestones, and clinical assessments is utilized to identify key features relevant to ASD diagnosis. These features include eye contact, gesture use, language skills, sensitivity to pain, communication abilities, and social interaction. Through fuzzy logic-based soft computing techniques, the model achieves enhanced accuracy in predicting ASD and assessing its severity in children. Sensitivity analysis highlights the significant contributions of input variables to ASD prediction, withsensitivity to pain, eye contact level, and social interaction emerging as crucial factors. Comparative analysis with the Back Propagation Algorithm underscores the superiority of the proposed Hybrid Algorithm in error minimization across various phases of model training and evaluation. The findings underscore the potential of adaptive neurofuzzy systems in facilitating early ASD diagnosis, enabling timely intervention and support for affected children and their families.This research contributes to advancing the understanding and management of ASD, offering valuable insights for clinical practice and research in pediatric neurodevelopmental disorders.
Autism Spectrum Disorder (ASD) is a neurological and developmental condition characterized by challenges in social interaction, communication (both verbal and non-verbal), and repetitive behaviours. While genetics play a key role in its onset, early diagnosis remains essential for effective intervention. Machine learning (ML) offers a promising approach to streamline and accelerate ASD detection, making it faster and more cost-effective than traditional methods. This paper evaluates eight classification models to identify key ASD features and automate diagnosis. We compare their performance on large datasets to enhance predictive accuracy. ML has transformed healthcare by leveraging vast data volumes for analysis, with technological advances over the past decade improving diagnostic tools now standard in medical settings. ASD affects individuals variably, with symptoms typically appearing between 18 months and 3 years. Although genetic and environmental factors contribute, no single cause is confirmed. Traditional screenings rely heavily on clinician expertise, involving manual assessments and scoring, which can be subjective and time-consuming—even experts face uncertainties in predicting onset or severity. Parents seek rapid, reliable results. ML and deep learning (DL) address these gaps by analyzing complex patterns in data, enabling early prediction of ASD and its severity. This study implements diverse algorithms to support precise, automated screening, reducing diagnostic delays and improving outcomes.
Devireddy Mamatha, K. Maheswari· 2026 6th International Confe...· 0 citations
The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process.
Shabeena Lylath, Laxmi B. Rananavare· IAES International Journal o...· 0 citations
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
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.
Neha A. Kandalkar, R. Jogekar· Adolescência e Saúde· 0 citations
This project introduces a smart, hybrid system that combines advanced deep learning technology with proven treatment methods, aiming to close the gap between diagnosis and meaningful help for autism, by blending advanced computational analysis with trusted treatment practices.
S. Ahmed, Shaikh Faeik, Shaikh Israhil et al.· International Journal for Re...· 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