An Efficient Autism Spectrum Disorder Classification in Different Age Groups using Machine Learning Models

Abstract

The current world has witnessed the emergence of various illnesses, such as autism spectrum disorder (ASD), that are not yet medically recognized. It impacts multiple behavioral domains, such as repetitive and stereotyped behavior, social competence, and linguistic skills. This condition is a severe neurodevelopmental disorder. It Identifying and classifying ASD is challenging and time-consuming due to its symptoms being remarkably similar to those of many other mental illnesses. Machine learning-based models are increasingly being used to predict a wide range of human diseases, leveraging various physiological and other characteristics. Our study aims to develop a classification model that can predict the likelihood of ASD in various age groups, such as toddlers, children, adolescents, and adults. We have utilized several machine learning (ML) algorithms, including support vector machine (SVM), Naive Bayes (NB), random forest (RF), extra trees classifier (ET), k-nearest neighbor (K-NN), decision tree (DT), Ada boost classifier (AB), and stochastic gradient descent (SGD) classifiers. These models are tested using four unique non-clinical ASD screening datasets that are publicly available from Kaggle and the UCI library. In the first dataset, there are 1054 instances and 19 features related to toddlers. The remaining ones consist of 21 traits and, for children, adolescents, and adults, 292, 104, and 704 cases, respectively. The outcomes of the experimentation have shown that the SDG, DT, and ET classifiers are the most commonly used models and have achieved results with almost 100% accuracy.