ArticleSN computer science2021
Detection of Autism Spectrum Disorder in Children Using Machine Learning Techniques.
Article in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled it
- Early Autism Spectrum Disorder Detection Using Adaptive Fused Spatial-Temporal Graph Convolutional Network Optimized With Artificial Lemming Algorithm.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis.Translational psychiatry · 2026Article
- Label leakage unmasked: a trustworthy-AI audit of autism screening models using the CLEAR-RD framework.Frontiers in public health · 2026Article
- Early autism detection: a review of emerging technologies, biomarkers, and explainable AI approaches.Molecular brain · 2025Review
- A Brief Observation to Screen Autism in Toddlers and Predict Developmental Trajectory.Journal of pediatrics. Clinical practice · 2025Article
- Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.Scientific reports · 2025Article
- An Adaptive Transfer Learning Framework for Multimodal Autism Spectrum Disorder Diagnosis.Life (Basel, Switzerland) · 2025Article
- A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.Journal of autism and developmental disorders · 2025Review
- Early Detection of Autism Spectrum Disorder Through Automated Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Explainable AI in early autism detection: a literature review of interpretable machine learning approaches.Discover mental health · 2025Review
- Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons.Scientific reports · 2025Article
- Artificial intelligence-driven diagnosis of autism spectrum disorder in children.Frontiers in medicine · 2025Article
- Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model.Frontiers in artificial intelligence · 2025Article
- Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening.Health information science and systems · 2024Article
- A deep learning model of dorsal and ventral visual streams for DVSD.Scientific reports · 2024Article
- Review
- A machine learning model based on CHAT-23 for early screening of autism in Chinese children.Frontiers in pediatrics · 2024Article
- Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism Spectrum Disorder (ASD) is a neurological disorder which might have a lifelong impact on the language learning, speech, cognitive, and social skills of an individual. Its symptoms usually show up in the developmental stages, i.e., within the first two years after birth, and it impacts around 1% of the population globally [https://www.autism-society.org/whatis/facts-and-statistics/. Accessed 25 Dec 2019]. ASD is mainly caused by genetics or by environmental factors; however, its conditions can be improved by detecting and treating it at earlier stages. In the current times, clinical standardized tests are the only methods which are being used, to diagnose ASD. This not only requires prolonged diagnostic time but also faces a steep increase in medical costs. To improve the precision and time required for diagnosis, machine learning techniques are being used to complement the conventional methods. We have applied models such as Support Vector Machines (SVM), Random Forest Classifier (RFC), Naïve Bayes (NB), Logistic Regression (LR), and KNN to our dataset and constructed predictive models based on the outcome. The main objective of our paper is to thus determine if the child is susceptible to ASD in its nascent stages, which would help streamline the diagnosis process. Based on our results, Logistic Regression gives the highest accuracy for our selected dataset.
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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.