Prediction of Autism Spectrum Disorder in the Early Stage Based on Machine Learning Approach

Abstract

Autism Spectrum Disorder (ASD) is a set of neurological impairments which are incurable but can be improved with early treatment. We obtained slightly earlier detected ASD datasets pertaining to children and highly processed the dataset as needed. Various ML approaches were applied to the collected dataset and compared their performance based on accuracy, precision, recall, f-measure, log loss, kappa statistics, and MCC. We found that DT provides the best performance with 100% accuracy. Then different FSTs methods were applied to the dataset to show the importance and identify the significant features responsible for ASD. The study's findings indicate that, when properly tuned, machine learning approaches can offer accurate forecasts of ASD status. According to the findings, the suggested model has the ability to diagnose ASD in its early phases.