Autism spectrum disorder detection with machine learning methods

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dc.authorid0000-0002-2481-0230en_US
dc.contributor.authorErkan, Uğur
dc.contributor.authorThanh, D.N.H.
dc.date.accessioned2021-01-10T06:54:51Z
dc.date.available2021-01-10T06:54:51Z
dc.date.issued2019
dc.departmentKMÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractAutistic Spectrum Disorder (ASD) is a disorder associated with genetic and neurological components leading to difficulties in social interaction and communication. According to statistics of WHO, the number of patients diagnosed with ASD is gradually increasing. Most of the current studies focus on clinical diagnosis, data collection and brain images analysis, but do not focus on the diagnosis of ASD based on machine learning. Objective: This study aims to classify ASD data to provide a quick, accessible and easy way to support early diagnosis of ASD. Methods: Three ASD datasets are used for children, adolescences and adults. To classify the ASD data, we used the k-Nearest Neighbours method (kNN), the Support Vector Machine method (SVM) and the Random Forests method (RF). In our experiments, the data was randomly split into training and test sets. The parts of the data were randomly selected 100 times to test the classification methods. Results: The final results were assessed by the average values. It is shown that SVM and RF are effective methods for ASD classification. In particular, the RF method classified the data with an accuracy of 100% for all above datasets. Conclusion: The early diagnosis of ASD is critical. If the number of data samples is large enough, we can achieve a high accuracy for machine learning-based ASD diagnosis. Among three classification methods, RF achieves the best performance for ASD data classification. © 2019 Bentham Science Publishers.en_US
dc.identifier.citationKarataş, K., Oral, B. (2020). An investigation into the readiness of elementary teacher candidates for culturally responsive teaching. Student Teaching: Perspectives, Opportunities and Challenges, 1-27.
dc.identifier.doi10.2174/2666082215666191111121115
dc.identifier.endpage308en_US
dc.identifier.issn2666-0830
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85079248369
dc.identifier.scopusqualityQ4
dc.identifier.startpage297en_US
dc.identifier.urihttps://doi.org/10.2174/2666082215666191111121115
dc.identifier.urihttps://hdl.handle.net/11492/4006
dc.identifier.volume15en_US
dc.identifier.wosWOS:000663627600008
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorErkan, Uğur
dc.language.isoen
dc.publisherBentham Science Publishersen_US
dc.relation.journalCurrent Psychiatry Research and Reviewsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutism Spectrum Disorderen_US
dc.subjectK-Nearest Neighbouren_US
dc.subjectMachine Learningen_US
dc.subjectRandom Foresten_US
dc.subjectSupervised Learningen_US
dc.subjectSupport Vector Machineen_US
dc.titleAutism spectrum disorder detection with machine learning methodsen_US
dc.typeArticle

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