Study and Research on Autism Spectrum Disorder Using Supervised Machine-Learning Techniques

Machine learning (ML) is embedded in everyday life and is used around the world by various organizations and in various fields. It has brought about a drastic difference in the healthcare industry. In addition, autism spectrum disorder (ASD) unknowingly affects people, but we are now predicting ASD in advance, on the basis of various behaviors, mainly the way that people interact/communicate with others, the way that they perform tasks, and their reactions. Detecting ASD at an early stage is essential to controlling it and preventing it from worsening. In this study, we incorporated child, adolescent, and adult datasets, and we used logistic regression, K-nearest number (KNN), random forest, support vector machine (SVM), and Naive Bayes to predict whether a person has ASD. To determine the accuracy of the above algorithm, we have used 20% data for testing and the remaining for training the algorithms. The outcomes of the various techniques are as follows: Logistic regression had an accuracy value of 95.0%, and KNN, random forest, SVM, and Naive Bayes had accuracy values of 84.72%, 96.36%, 86.36%, and 95.15%, respectively.