An Efficient Autism Spectrum Disorder Classification in Different Age Groups - Paper - Machine Learning-Based Approach

Aug 4, 2026

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Autism spectrum disorder (ASD) is a severe neurodevelopmental condition with symptoms that overlap other mental illnesses, making clinical identification and classification time-consuming and difficult. Machine learning models can support ASD screening by learning from physiological and related characteristics. This study develops a classification framework to predict ASD likelihood across toddlers, children, adolescents, and adults using multiple algorithms, and evaluates performance on four publicly available non-clinical datasets from Kaggle and UCI, reporting near-100% accuracy for selected models.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Autism spectrum disorder challenges and background

Motivation for machine learning classification

Methodology

Datasets and age-group feature sets

Machine learning models used

Results

Model performance and accuracy trends

Conclusion

Key findings and implications

JOE International Journal of Online and Biomedical Engineering [Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org) iJOE | eISSN: 2626-8493 | Vol. 20 No. 9 (2024) | [https://doi.org/10.3991/ijoe.v20i09.48831](https://doi.org/10.3991/ijoe.v20i09.48831) PAPER An Efficient Autism Spectrum Disorder Classification in Different Age Groups using Machine Learning Models Ambika Rani Subhash1,2(􀀍), Ashwin Kumar U Motagi2 1Department of Information Science and Engineering, BMS Institute of Technology and Management, Bengaluru, Karnataka, India 2School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India [ambikasubash@bmsit.in](ambikasubash@bmsit.in) ABSTRACT The current world has witnessed the emergence of various illnesses, such as autism spectrum disorder (ASD), that are not yet medically recognized. It impacts multiple behavioral domains, such as repetitive and stereotyped behavior, social competence, and linguistic skills. This condition is a severe neurodevelopmental disorder. It Identifying and classifying ASD is challenging and time-consuming due to its symptoms being remarkably similar to those of many other mental illnesses. Machine learning-based models are increasingly being used to predict a wide range of human diseases, leveraging various physiological and other characteristics. Our study aims to develop a classification model that can predict the likelihood of ASD in various age groups, such as toddlers, children, adolescents, and adults. We have utilized several machine learning (ML) algorithms, including support vector machine (SVM), Naive Bayes (NB), random forest (RF), extra trees classifier (ET), k-nearest neighbor (K-NN), decision tree (DT), Ada boost classifier (AB), and stochastic gradient descent (SGD) classifiers. These models are tested using four unique non-clinical ASD screening datasets that are publicly available from Kaggle and the UCI library. In the first dataset, there are 1054 instances and 19 features related to toddlers. The remaining ones consist of 21 traits and, for children, adolescents, and adults, 292, 104, and 704 cases, respectively. The outcomes of the experimentation have shown that the SDG, DT, and ET classifiers are the most commonly used models and have achieved results with almost 100% accuracy. KEYWORDS autism spectrum disorder (ASD), machine learning (ML), support vector machine (SVM), naive bayes (NB), random forest (RF), extra trees (ET), k-nearest neighbor (K-NN), decision tree (DT), Ada boost (AB), stochastic gradient descent (SGD) 1 INTRODUCTION We have observed numerous diseases that cannot be clinically diagnosed, among which autism spectrum disorder (ASD) is one example. This condition affects many behavioral domains, such as social and communication skills, as well as stereotyped Subhash, A. R., Motagi, A. K. U. (2024) . An Efficient Autism Spectrum Disorder Classification in Different Age Groups using Machine Learning Models. International Journal of Online and Biomedical Engineering (iJOE), 20(9), pp. 17–38. [https://doi.org/10.3991/ijoe.v20i09.48831](https://doi.org/10.3991/ijoe.v20i09.48831)[ ](https://doi.org/10.3991/ijoe.v20i09.48831)[Article submitted 2024-02-28. Revision uploaded 2024-04-19. Final acceptance 2024-04-19.](Article submitted 2024-02-28. Revision uploaded 2024-04-19. Final acceptance 2024-04-19.) © 2024 by the authors of this article. Published under CC-BY. iJOE | Vol. 20 No. 9 (2024) International Journal of Online and Biomedical Engineering (iJOE) 17 Subhash and Motagi and repetitive behaviors. It is a significant neurodevelopmental disorder [1] . A set of neurological conditions known as ASDs impede the brain’s normal development [2] . ASD can lead to social challenges, sensory issues, repetitive behaviors, and intellectual disabilities. Psychiatric or neurological conditions like hyperactivity, attention deficit disorder, an

FAQs

Why is autism spectrum disorder (ASD) difficult to classify using conventional approaches?

ASD symptoms closely resemble those of other mental illnesses, making identification and classification challenging and time-consuming.

Which machine learning algorithms are used in the study?

The study evaluates multiple ML algorithms including SVM, Naive Bayes, random forest, extra trees, k-nearest neighbor, decision tree, Ada boost, and stochastic gradient descent.

How are the models evaluated and on what data?

Models are tested using four publicly available non-clinical ASD screening datasets obtained from Kaggle and the UCI library, covering different age groups.

Aug 4, 2026

Upload by Maeve