The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups

Abstract

The world has seen the advent of numerous illnesses that cannot be medically recognized, such as Autism Spectrum Disorder (ASD). It affects several behavioral domains, including social and linguistic competence and stereotyped and repetitive actions. This illness is a serious neurodevelopmental disorder. Since many other mental illnesses have strikingly similar symptoms to those of ASD, diagnosing ASD can be difficult and time-consuming. Early diagnosis based on different health and physiological characteristics seems feasible with the rising usage of machine learning-based models in predicting many human diseases. This study aims to create a classification model that can predict the likelihood of ASD with the greatest degree of precision. To investigate the potential for predicting and analyzing ASD traits in the Toddler, Child, Adolescent, and adult age groups, we used several supervised Machine Learning (ML) models. These include Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF). Four publicly available, distinctive non-clinical ASD screening datasets from Kaggle and the UCI machine learning library are used to test these models. The first dataset includes 1054 instances and 19 toddler-related features. The remaining ones consist of 21 traits and 292, 104, and 704 cases involving children, adolescents, and adults, respectively. After implementing different ML approaches over the pre-processing datasets, the results showed that the DT, LR, and RF classifiers are the dominant models. These dominated models achieve the highest prediction accuracy, among other studied models, of about 100% for all the utilized datasets.