Detection of autism spectrum disorder using multilayer perceptron classifier

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by distinctive challenges in both verbal and nonverbal communication, social interaction, and repetitive behaviors. However, the diagnosis of ASD usually occurs within a clinical setting, conducted by licensed professionals, and often involves lengthy and costly procedures. On the other hand, machine learning holds significant promise for improving diagnostic and intervention research within the behavioral sciences, particularly in research concerning ASD disease. Hence, a deep investigation of a machine learning algorithm for ASD detection is crucial. Therefore, this paper presented a new system for differentiating the ASD samples from non-ASD (i.e., healthy) samples. The samples of ASD have been compiled from toddlers. The multilayer perceptron (MLP) algorithm is used to classify ASD samples from non-ASD samples. The proposed MLP classifier is implemented based on different numbers of neurons (i.e., nodes). In other words, the proposed MLP classifier started with 10 neurons and finished with 50 neurons with an increment step of 5 neurons. The outcomes demonstrate that the MLP classifier acquired different results concerning the number of neurons. The MLP obtained the best performance, reaching an accuracy rate of 100% in identifying ASD cases.