{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:01:21Z","timestamp":1776369681177,"version":"3.51.2"},"reference-count":58,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely impairs an individual\u2019s cognitive, linguistic, object recognition, communication, and social abilities. This situation is not treatable, although early detection of ASD can assist to diagnose and take proper steps for mitigating its effect. Using various artificial intelligence (AI) techniques, ASD can be detected an at earlier stage than with traditional methods. The aim of this study was to propose a machine learning model that investigates ASD data of different age levels and to identify ASD more accurately. In this work, we gathered ASD datasets of toddlers, children, adolescents, and adults and used several feature selection techniques. Then, different classifiers were applied into these datasets, and we assessed their performance with evaluation metrics including predictive accuracy, kappa statistics, the f1-measure, and AUROC. In addition, we analyzed the performance of individual classifiers using a non-parametric statistical significant test. For the toddler, child, adolescent, and adult datasets, we found that Support Vector Machine (SVM) performed better than other classifiers where we gained 97.82% accuracy for the RIPPER-based toddler subset; 99.61% accuracy for the Correlation-based feature selection (CFS) and Boruta CFS intersect (BIC) method-based child subset; 95.87% accuracy for the Boruta-based adolescent subset; and 96.82% accuracy for the CFS-based adult subset. Then, we applied the Shapley Additive Explanations (SHAP) method into different feature subsets, which gained the highest accuracy and ranked their features based on the analysis.<\/jats:p>","DOI":"10.3390\/a15050166","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T13:06:23Z","timestamp":1652706383000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2885-3107","authenticated-orcid":false,"given":"Mousumi","family":"Bala","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Eastern University, Ashulia Model Town, Savar 1345, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6511-3063","authenticated-orcid":false,"given":"Mohammad Hanif","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jahangirnagar University, Savar 1342, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1007-572X","authenticated-orcid":false,"given":"Md. Shahriare","family":"Satu","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, Noakhali Science and Technology University, Sonapur 3814, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8008-8203","authenticated-orcid":false,"given":"Khondokar Fida","family":"Hasan","sequence":"additional","affiliation":[{"name":"Centre for Cyber Security Research & Innovation, RMIT University, 124 La Trobe Street, Melbourne, VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"Artificial Intelligence & Digital Health, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1111\/j.1467-8624.2012.01870.x","article-title":"Developmental trajectories in children with and without autism spectrum disorders: The first 3 years","volume":"84","author":"Landa","year":"2013","journal-title":"Child Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9228","DOI":"10.1523\/JNEUROSCI.3340-04.2004","article-title":"Autism and abnormal development of brain connectivity","volume":"24","author":"Belmonte","year":"2004","journal-title":"J. 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