Early detection of autism spectrum disorder through hybrid deep learning and classical machine learning approaches

Data‐Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets

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.

Deep learning algorithms for autism spectrum disorder detection using eye-tracking patterns

This study investigates the utilization of deep learning models to recognize ASD among 13-year-old children based on eye movement data collected as participants observed static images and short video sequences, highlighting the potential of deep learning frameworks as objective, data-driven tools for ASD detection in both clinical and research contexts.

Enhancing Autism Spectrum Disorder Diagnosis through Behavioral Pattern Recognition with Deep Learning Approaches

The findings indicate that deep learning-based behavioural analysis can serve as a robust and scalable alternative to manual diagnostic assessments and support the use of patent-driven deep learning behavioral analytics as a promising assistive tool for early screening and objective assessment in clinical environments.

Enhancing early detection of autism spectrum disorder through ensemble-based machine learning classifiers

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.

Early Detection of

Advanced Computational Approaches for Early detection of Autism Spectrum Disorder using Machine and Deep Learning: Recent Trends and Perspective

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.