A SURVEY ON PREDICTION OF AUTISM SPECTRUM DISORDER USING DATA SCIENCE TECHNIQUES

Abstract

Autism Spectrum Disorder is a lifelong brain developmental disorder. Diagnosing the level of Autism and predicting the severity of the same are too complex, and it requires a depth analysis of the historical data on the autism patient. Nowadays, Data science techniques play a vital role in diagnosing autism. Decision Tree, Random Forest, Logistic Regression, Adaboost, Naïve Bayse, K-Nearest Neighbour, Support Vector Machine and etc., are the few techniques labeled under the roof of data science are used to predict such disorders. The paper aims to present a survey on the various models proposed by various researchers to predict the severity of autism using data science techniques.