High-level classification using complex networks for Autism Spectrum Disorder detection
Author:
Araújo Lucas G. T.,Sabino-Silva Robinson,Carneiro Murillo G.
Abstract
The diagnosis of Autism Spectrum Disorder (ASD) is typically based on behavioral observation, which is a process time-consuming, subjective and reliant on professional judgment. This study leverages research on salivary biomarkers to develop a tool capable of adding objectivity to this process. A high-level classifier based on complex networks was employed using different network formation methods based on Attenuated Total Reflection Fourier-Transform Infrared spectroscopy (ATR-FTIR) data from saliva samples. The results indicate the use of high-level classifiers as a promising tool for ASD detection.
Publisher
Sociedade Brasileira de Computação - SBC
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