Title: Predictive assessment of autism using unsupervised machine learning models

Authors: Anju Pratap; C.S. Kanimozhiselvi

Addresses: Department of Information Technology, Anna University, Madras Institute of Technology Campus, Chrompet, Chennai-600044, India ' Department of Computer Science and Engineering, Kongu Engineering College, Erode-638052, India

Abstract: The application of different artificial intelligence models in clinical decision support systems has been a research topic which mainly focuses on the diagnosis method. In this paper we describe the application of unsupervised machine learning models in decision supportive tools for predictive grading of autistic disorder. We used competitive learning networks and unsupervised data clustering methods to model the differential grading in childhood autistic rating scale (CARS)-based assessment. Modelling of conventional score-based assessment using unsupervised learning methods is the novelty in this work. Self-organisation feature map (SOM) with single input and four output units perform with a predictive ability of 100% during resubstitution testing.

Keywords: autism prediction; unsupervised learning; K-meansclustering; fuzzy C means clustering; self-organisation maps; SOM; learning vector quantisation; LVQ; predictive assessment; modelling; clinical DSS; decision support systems; unsupervised machine learning; predictive grading; competitive learning networks; differential grading; childhood autistic rating scale; self-organising; map; SOM.

DOI: 10.1504/IJAIP.2014.062174

International Journal of Advanced Intelligence Paradigms, 2014 Vol.6 No.2, pp.113 - 121

Published online: 28 Jun 2014 *

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