Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 1294-1300· 0 citations· 28 references
Abstract
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.
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
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) is a heterogeneous neurodevelopmental condition characterized by diverse behavioral, cognitive, sensory, and communication profiles, making early diagnosis and personalized intervention challenging. Recent advances in machine learning (ML) and deep learning (DL) have enabled the development of computational tools for ASD screening, classification, severity assessment, and intervention monitoring. This review synthesizes findings from 50 recent studies that applied ML and DL techniques to ASD-related datasets, including electroencephalography (EEG), eye-tracking, behavioral video, microbiome, voice acoustic, demographic, and multimodal data. The review addresses three key questions: (i) which data modalities and computational approaches are most frequently used, (ii) how diagnostic performance is evaluated across different study designs, and (iii) what methodological challenges limit clinical translation. The literature is organized according to data modality, algorithmic approach, and clinical readiness. Approaches examined include conventional ML methods, convolutional neural networks, graph neural networks, hybrid deep learning architectures, federated learning, explainable artificial intelligence, topological data analysis, and multimodal fusion. The findings suggest that multimodal and graph-based approaches provide a more comprehensive representation of ASD phenotypes than single-modality methods. Explainability and privacy-preserving learning have also emerged as important considerations for clinical deployment. However, many reported high-performance models are based on small sample sizes, repeated use of the ABIDE dataset, class imbalance, single-site validation, or limited external testing, raising concerns regarding generalizability. Beyond diagnostic accuracy, this review evaluates model interpretability, calibration, scalability, validation rigor, and clinical applicability. Overall, the analysis highlights the need for standardized benchmarks, externally validated multimodal datasets, clinically relevant evaluation metrics, and decision-support systems that complement rather than replace expert clinical assessment in ASD diagnosis and management.
S. K, Lakshmi Annapurna Y· Journal of Visualized Experi...· 0 citations
A comparative analysis of recent approaches for early ASD prediction, focusing on methodologies, datasets, predictive performance, and key limitations indicates that deep learning-based approaches, particularly those using facial images and behavioral video analysis, generally achieve higher accuracy than traditional machine learning models.
Gaurangkumar Patel, H.B. Pandya, B. Trivedi· SPU - Journal of Science, Te...· 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
Abstract Background Diagnosing autism spectrum disorder (ASD) in adulthood is time-consuming and markedly complicated by the requirement to distinguish between ASD and differential diagnoses also associated with social interaction difficulties, such as borderline personality disorder (BPD)—a distinction for which currently no valid screening or diagnostic tool exists. While technology-assisted diagnostics (TAD) has emerged, existing algorithms have focused on classifying between ASD and no diagnosis, not fully addressing clinical reality. Objective Therefore, we assessed the feasibility of TAD for differential diagnostics by classifying between ASD and BPD in this proof-of-concept study. Methods We collected a rich multimodal dataset of reciprocal interactions, specifically dyadic conversations (n=120 interaction partners). From this data, we extracted more than 800 features, allowing us to capture the core area of defining symptoms for both conditions: social interactions. These features include speech patterns, facial expressions, movement quantity and interpersonal synchrony. We used these features to train and stack linear support vector machines to classify between ASD-involved, BPD-involved and comparison interaction partners. Findings Base models capturing facial expressions during speaking and listening, speech patterns, synchronisation of facial expressions and movement quantity all performed above chance when differentiating between ASD-involved and BPD-involved interaction partners. Stacking all base models containing conceptually related features further increased accuracy, with our algorithm achieving nearly 82% of balanced accuracy, solely based on 20 min of conversation. Conclusions Our proof-of-concept study shows the immense potential of TAD for differential diagnostics: data collection only requires microphones and webcams while feature-extraction is automated, making this approach highly objective, scalable and user-friendly. Clinical implications Our TAD algorithm shows the potential of multimodal, behavioural data for differential diagnostics. On the basis of clinical validation such an algorithm has the potential to streamline differential diagnoses of ASD in the future, enabling faster and more accurate diagnostic assessment and ultimately reducing patient distress by shortening the wait for an appropriate treatment plan.
I. S. Plank, J. C. Koehler, J. Eckelmann et al.· BMJ mental health· 0 citations