ArticleJournal of healthcare engineering2022
Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review.Frontiers in psychiatry · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Wireless Electroencephalography in Research on Children With Developmental Disabilities: Scoping Review.Journal of medical Internet research · 2026Article
- NeuroMimicNet: A Multimodal Neuromorphic Framework for Early Screening and Cognitive Pattern Recognition in Children with Autism.Journal of multidisciplinary healthcare · 2026Article
- AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.Frontiers in computational neuroscience · 2026Article
- Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.Epigenomics · 2025Article
- The Current Research Landscape on the Machine Learning Application in Autism Spectrum Disorder: A Bibliometric Analysis From 1999 to 2023.Current neuropharmacology · 2025Article
- Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder.Health information science and systems · 2024Article
- A Closed-Loop Ear-Worn Wearable EEG System with Real-Time Passive Electrode Skin Impedance Measurement for Early Autism Detection.Sensors (Basel, Switzerland) · 2024Article
- Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.Healthcare technology letters · 2024Article
- Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study.Healthcare (Basel, Switzerland) · 2024Article
- Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study.Frontiers in neuroscience · 2024Article
- A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features.Diagnostics (Basel, Switzerland) · 2023Article
- Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.