A Comparative Analysis of Prediction of Autism Spectrum Disorder (ASD) using Machine Learning
V. Vishal, Abhishek Kumar Singh, Y. Bevish Jinila, C. Kavitha, S. Prayla Shyry, J. Jabez
Abstract
The Autism Spectrum Disorder (ASD) is a neurological disease, which affects the mental, social and physical state of a person. A person of any age group can be found infected by it. It is very difficult to identify, if a person is the victim of this disorder. Classical approaches that find the occurrence of autism in a person is time consuming and expensive. Machine learning approaches, on the other hand, have paved the way for intelligent diagnostics. This paper focusses on identification of specific traits that helps to automate the diagnosis process and further evaluate and perform a comparative analysis of various machine learning algorithms namely K-Nearest Neighbour, Logistic Regression, SVM and Naïve Bayes, to predict the occurrence of autism disorder. Experimental analysis shows that the Naïve Bayes algorithm provides a better accuracy of 99.6% compared to other algorithms.