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A Clustering Approach for Autism based Autistic Trait Classification

Baadel, Said, Thabtah, Fadi and Lu, Joan (2019) A Clustering Approach for Autism based Autistic Trait Classification. Informatics for Health and Social Care, 45 (3). pp. 309-326. ISSN 1753-8165

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Machine learning (ML) techniques can be utilized by physicians, clinicians, as well as other users, to discover Autism Spectrum Disorder (ASD) symptoms based on historical cases and controls to enhance autism screening efficiency and accuracy. The aim of this study is to improve the performance of detecting ASD traits by reducing data dimensionality and eliminating redundancy in the autism dataset. To achieve this, a new semi-supervised ML framework approach called Clustering-based Autistic Trait Classification (CATC) is proposed that uses a clustering technique and validation of the classifiers is done by classification techniques. The proposed method identifies potential autism cases based on their similarity traits as opposed to a scoring function used by many ASD screening tools. Empirical results on different datasets involving children, adolescents, and adults were verified and compared to other common machine learning classification techniques. The results showed that CATC offers classifiers with higher predictive accuracy, sensitivity, and specificity rates than those of other intelligent classification approaches such as Artificial Neural Network (ANN), Random Forest, and Random Trees, and Rule Induction. These classifiers are useful as they are exploited by diagnosticians and other stakeholders involved in ASD screening.

Item Type: Article
Uncontrolled Keywords: Autism Diagnosis; Classification; Clustering; Machine Learning; OMCOKE; Predictive Models
Subjects: A General Works > AI Indexes (General)
H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
H Social Sciences > HV Social pathology. Social and public welfare
L Education > L Education (General)
Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
Depositing User: Said Baadel
Date Deposited: 08 Oct 2019 10:05
Last Modified: 28 Aug 2021 14:11


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