RETRACTED CHAPTER: A Survey on Early Prediction of Autism Spectrum Disorder Using Supervised Machine Learning Methods

Abstract

Autism is a neurobehavioral problem that debilitates the capacity to convey and interact with others (Reeta et al. Predicting Autism Using Naive Bayesian Classification Approach. In 2018 International Conference on Communication and Signal Processing (ICCSP), pp. 0109–0113. IEEE, 2018). Autistic Spectrum Disorder (ASD) is a mental disorder that hinders procurement of etymological, communication, cognitive, social skills, and stereotypical motor behaviors and capabilities. In the previous decades, ASD has been searched by public and computational intelligence scientists exploiting innovative technologies like machine learning for early prediction and hence to reduce the diagnostic timing, with high precision and enhanced quality of diagnosis. Machine learning is a multidisciplinary research field that employs intelligent techniques in the form of algorithm to determine beneficial secret outlines, which are subjugated in prediction to enhance the decision-making. The strategies of machine learning like vector machines, choice trees, calculated relapses, and others have been applied to datasets identified with chemical imbalance to theory prescient models. These models privilege to enrich the capability of Doctors to provide healthy diagnoses and predictions of ASD. This paper gives an extensive audit of not many papers utilizing administered AI in ASD which incorporates calculations for arrangement and forecast. The aim of the paper is to describe the different supervised machine learning techniques which is suitable for early prediction of ASD.