Autism detection using machine learning Online publication date: Tue, 05-Oct-2021
by U.B. Mahadevaswamy; Rohan Ravikumar; Rachana Mahadev; Kadaparthi Varun Rao; K.S. Anurag
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 17, No. 4, 2021
Abstract: Autism is one of the most eminent bio-neurological developmental disorders characterised by disablement in social-communication and behavioural aspects like repetitive behaviours and restrictive interests. Present methods of autism detection are based on behavioural analysis of the subject. These methods are dependent on the skills of the examiner and are laborious. This paper proposes a method for autism detection by virtue of structural MRI of brain obtained from the ABIDE-2 dataset and well-trained machine learning models. The prediction was done based on supervised learning methods namely Random Forest and Logistic Regression. These methods yielded accuracy of 0.82 and 0.80 respectively which are comparatively better than previous works. VBM analysis was conducted on the same dataset with SPM8 in order to obtain the difference in brain structure of typical controls and autism affected subjects and a resultant glass brain image was obtained.
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