Aug 2026· IAES International Journal of Artificial Intelligence (IJ-AI)· Vol 15, pp. 3732· 0 citations· 31 references
TL;DR
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.
Abstract
Autism spectrum disorder (ASD) is a developmental disability characterized by significant social, communication, and behavioral challenges. Machine learning is a practical approach for autism detection. 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. This ensemble approach is designed to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process. This methodology addresses the urgent need for early and accurate ASD identification, enabling timely interventions. Leveraging complex data analysis, it offers deeper diagnostic insights, facilitating informed clinical decisions and advancing ASD research. The methodology's accessibility across healthcare settings marks a significant step forward in making early ASD detection more universally available, showcasing the transformative potential of machine learning in healthcare. In deploying the “ensemble-based machine learning classifier” for ASD diagnosis, this study utilizes an extensive dataset comprising behavioral and medical profiles from diverse demographics, including toddlers, children, adolescents, and adults with ASD. Upon the preliminary analysis, the dataset enables the methodology to learn from a wide array of ASD manifestations, ensuring its robustness and applicability across different age groups and severity levels.
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 need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.
Anupama N, Chandrashekar M. Patil· International Journal of Dev...· 0 citations
Early detection of autism spectrum disorder (ASD) is essential for timely intervention. This study presents a hybrid artificial intelligence framework for non-invasive ASD pre-screening using children’s coloring, drawing, and handwriting activities. The proposed framework combines deep convolutional neural networks (VGG16, ResNet50, and EfficientNetB0) as feature extractors with a support vector machine (SVM) classifier to distinguish four diagnostic categories: non-ASD, mild ASD, moderate ASD, and severe ASD. Experimental results demonstrate task-specific performance across architectures. ResNet50–SVM achieved perfect classification for coloring tasks, with 100% accuracy, precision, recall, and F1-score. VGG16–SVM performed best for drawing, achieving 88% accuracy and recall, 89% precision, and an F1-score of 87%. EfficientNetB0–SVM produced the highest handwriting performance, achieving 96% across all evaluation metrics. These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool. Future work 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
Based on the evaluated studies, transfer learning with diverse datasets and modalities has great promise for early ASD diagnosis, and a hybrid transfer learning-based framework is advised to assist clinicians and therapists in accurately diagnosing and assessing ASD severity.
R. Thillaikarasi, P. Kumaresan· International Conference on...· 0 citations
Autism Spectrum Disorder (ASD) diagnosis increasingly benefits from
automated behavioral analysis, particularly for identifying self-stimulatory behaviors that are critical
clinical indicators. This proposed patent-oriented methodology presents a deep learning-based
behavioral pattern recognition framework designed to detect and classify self-stimulatory actions
using a curated Self-Stimulatory Behaviour Dataset. The proposed model learns discriminative
temporal and spatial behavioral features to improve diagnostic reliability. Experimental evaluation
demonstrates strong classification performance, achieving an accuracy of 95.8%, precision of 94.9%,
recall of 95.2%, and an F1-score of 95.0%. The results indicate robust generalization across
behavioral variations and highlight the framework’s ability to distinguish subtle repetitive actions
associated with ASD. These findings support the use of patent-driven deep learning behavioral
analytics as a promising assistive tool for early screening and objective assessment in clinical
environments.
A Convolutional Neural Network (CNN) architecture was developed and trained using the
SSBD dataset, which contains comprehensive behavioural data of individuals with and without ASD.
The model was designed to identify subtle behavioural cues and non-linear relationships that may not
be evident through traditional assessment methods. Performance metrics, including accuracy,
precision, recall, and F1-score, were computed and compared with results from conventional
diagnostic approaches.
The proposed CNN model demonstrated a notable improvement in diagnostic accuracy and
efficiency over traditional clinical methods. The model effectively recognized distinctive behavioural
indicators associated with ASD, achieving high classification performance across all evaluation
metrics. The deep learning approach successfully captured complex behavioural dependencies,
minimizing diagnostic subjectivity and variability.
The findings indicate that deep learning-based behavioural analysis can serve as a robust
and scalable alternative to manual diagnostic assessments. By leveraging large-scale behavioural
datasets, the model offers clinicians data-driven insights, enabling earlier and more objective
detection of ASD. This approach also highlights the potential of artificial intelligence in bridging
existing gaps in neurodevelopmental diagnostics.
This study presents a novel CNN-based framework for automated ASD diagnosis using
behavioural pattern recognition. The model’s superior accuracy and efficiency suggest its potential
for clinical integration, allowing earlier interventions and improved therapeutic outcomes for
individuals with ASD. Future research will explore model generalization across diverse populations
and real-time behavioural monitoring systems.
Syed Farzana, Ramkumar Devendiran· Recent Patents on Engineerin...· 0 citations
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.
Nneka MaryAnn Okafor, C. Ituma, R. Nweze· Communication in Physical Sc...· 0 citations