Authors: Fuad M. Alkoot; Abdullah K. Alqallaf
Addresses: Higher Institute of Telecommunication & Navigation, PAAET, Alshuhada, Kuwait ' Electrical Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat, Kuwait
Abstract: Automated autism detection is needed to facilitate urgently required therapy. However, contrary to cancer, autism detection using genetic data has not attracted much attention. In this paper, we investigate autism detection using machine learning techniques. The main goal is to test whether genetic data with machine learning tools can result in an abbreviated and accurate instrument for classification of autism. For this, a system comprising four stages is proposed, where at each stage, we experiment with different feature reduction, classification and combination methods to find if it is possible to detect autism. The experimental results show that our classifier-based system can achieve optimum accuracy of early screening. We achieved optimum accuracy when examined on independent and unseen test data. The optimum performance was mostly achieved using a three-layer back-propagation neural network classifier combined using the feature selection-based combiner. This was achievable only when the data dimensionality was reduced using our proposed feature selection method. The maximum number of features varied for the different chromosomes and ranged between 150 and 500.
Keywords: classifiers; autism detection; feature reduction; combination; feature selection; neural networks; nearest neighbour; CGH data; DNA copy number variation; machine learning; bioinformatics; classification; early screening.
International Journal of Data Mining and Bioinformatics, 2016 Vol.16 No.2, pp.141 - 169
Accepted: 11 Sep 2016
Published online: 25 Oct 2016 *