{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:38:50Z","timestamp":1778344730702,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU)","award":["RP-21-09-09"],"award-info":[{"award-number":["RP-21-09-09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient\u2019s expensive medical costs and lengthy examinations. We developed a machine learning (ML) architecture that is capable of effectively analysing autistic children\u2019s datasets and accurately classifying and identifying ASD traits. We considered the ASD screening dataset of toddlers in this study. We utilised the SMOTE method to balance the dataset, followed by feature transformation and selection methods. Then, we utilised several classification techniques in conjunction with a hyperparameter optimisation approach. The AdaBoost method yielded the best results among the classifiers. We employed ML and statistical approaches to identify the most crucial characteristics for the rapid recognition of ASD patients. We believe our proposed framework could be useful for early diagnosis and helpful for clinicians.<\/jats:p>","DOI":"10.3390\/computers12050092","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T13:54:46Z","timestamp":1682949286000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["An Integrated Statistical and Clinically Applicable Machine Learning Framework for the Detection of Autism Spectrum Disorder"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8483-7059","authenticated-orcid":false,"given":"Md. Jamal","family":"Uddin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1640-6649","authenticated-orcid":false,"given":"Md. Martuza","family":"Ahamad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}]},{"given":"Prodip Kumar","family":"Sarker","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8866-743X","authenticated-orcid":false,"given":"Sakifa","family":"Aktar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}]},{"given":"Naif","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5507-9399","authenticated-orcid":false,"given":"Salem A.","family":"Alyami","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6798-6535","authenticated-orcid":false,"given":"Muhammad Ashad","family":"Kabir","sequence":"additional","affiliation":[{"name":"School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"Artificial Intelligence & Data Science, 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":[[2023,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3761","DOI":"10.1007\/s10803-018-3639-1","article-title":"Autism diagnosis in the United Kingdom: Perspectives of autistic adults, parents and professionals","volume":"48","author":"Crane","year":"2018","journal-title":"J. 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