{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:59:17Z","timestamp":1781884757914,"version":"3.54.5"},"reference-count":57,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T00:00:00Z","timestamp":1548720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:p>Autism spectrum disorder is a developmental disorder that describes certain challenges associated with communication (verbal and non-verbal), social skills, and repetitive behaviors. Typically, autism spectrum disorder is diagnosed in a clinical environment by licensed specialists using procedures which can be lengthy and cost-ineffective. Therefore, scholars in the medical, psychology, and applied behavioral science fields have in recent decades developed screening methods such as the Autism Spectrum Quotient and Modified Checklist for Autism in Toddlers for diagnosing autism and other pervasive developmental disorders. The accuracy and efficiency of these screening methods rely primarily on the experience and knowledge of the user, as well as the items designed in the screening method. One promising direction to improve the accuracy and efficiency of autism spectrum disorder detection is to build classification systems using intelligent technologies such as machine learning. Machine learning offers advanced techniques that construct automated classifiers that can be exploited by users and clinicians to significantly improve sensitivity, specificity, accuracy, and efficiency in diagnostic discovery. This article proposes a new machine learning method called Rules-Machine Learning that not only detects autistic traits of cases and controls but also offers users knowledge bases (rules) that can be utilized by domain experts in understanding the reasons behind the classification. Empirical results on three data sets related to children, adolescents, and adults show that Rules-Machine Learning offers classifiers with higher predictive accuracy, sensitivity, harmonic mean, and specificity than those of other machine learning approaches such as Boosting, Bagging, decision trees, and rule induction.<\/jats:p>","DOI":"10.1177\/1460458218824711","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T07:12:15Z","timestamp":1548745935000},"page":"264-286","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":233,"title":["A new machine learning model based on induction of rules for autism detection"],"prefix":"10.1177","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2664-4694","authenticated-orcid":false,"given":"Fadi","family":"Thabtah","sequence":"first","affiliation":[{"name":"University of Huddersfield, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Peebles","sequence":"additional","affiliation":[{"name":"University of Huddersfield, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,1,29]]},"reference":[{"key":"bibr1-1460458218824711","volume-title":"Autism spectrum disorder screening instruments for very young children: a systematic review","author":"Towle P","year":"2016"},{"issue":"2","key":"bibr2-1460458218824711","first-page":"1","volume":"63","author":"Centers for Disease Control and Prevention","year":"2014","journal-title":"MMWR"},{"key":"bibr3-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1007\/BF02172145"},{"key":"bibr4-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1023\/A:1005592401947"},{"key":"bibr5-1460458218824711","doi-asserted-by":"publisher","DOI":"10.5772\/65906"},{"key":"bibr6-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1177\/1362361315573636"},{"key":"bibr7-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1023\/A:1005647629002"},{"key":"bibr8-1460458218824711","volume-title":"(CARS\u2122-2) Childhood Autism Rating Scale\u2122, Second Edition","author":"Schopler E","year":"2010"},{"key":"bibr9-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1007\/BF02408436"},{"key":"bibr10-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1023\/A:1005653411471"},{"key":"bibr11-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaac.2011.11.003"},{"key":"bibr12-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0043855"},{"issue":"5","key":"bibr13-1460458218824711","first-page":"1","volume":"45","author":"Bone D","year":"2014","journal-title":"J Autism Dev Disord"},{"key":"bibr14-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2015.221"},{"key":"bibr15-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1111\/jcpp.12559"},{"key":"bibr16-1460458218824711","first-page":"1","volume-title":"Proceedings of the 1st international conference on medical and health informatics 2017","author":"Thabtah F."},{"key":"bibr17-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1504\/IJAIP.2014.062174"},{"key":"bibr18-1460458218824711","first-page":"63","volume-title":"Federated conference on computer science and information systems","author":"Pancers K"},{"key":"bibr19-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2015.7"},{"key":"bibr20-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1186\/s13229-017-0180-6"},{"issue":"7","key":"bibr21-1460458218824711","first-page":"11","volume":"9","author":"Al-Diabat M.","year":"2018","journal-title":"Int J Adv Comput Sci Appl"},{"key":"bibr22-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ifs.2013.0202"},{"key":"bibr23-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888907001026"},{"key":"bibr24-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"bibr25-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116251"},{"key":"bibr26-1460458218824711","first-page":"115","volume-title":"Proceedings of the twelfth international conference on machine learning","author":"Cohen W"},{"key":"bibr27-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"bibr28-1460458218824711","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"bibr29-1460458218824711","first-page":"35","volume-title":"Pacific-Asia conference on knowledge discovery and data mining","author":"Mohammad R"},{"issue":"3","key":"bibr30-1460458218824711","first-page":"233","volume":"2","author":"Qabajeh I","year":"2015","journal-title":"J Manage Anal"},{"key":"bibr31-1460458218824711","unstructured":"Thabtah F. 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