Deep Analysis of Autism Spectrum Disorder Detection Techniques

Abstract

Autism also called as Autism spectrum disorder (ASD) is a complex, complicated and lifelong development disability which includes problem that are characterized by repetitive behavior, non-verbal communication, lack of concentration. In recent years, ASD is increasing at a higher momentum which needs early diagnosis. Detecting Autism through various Screening tool are very time consuming and costly. In last few year, various mathematical models also called as predictive analytics are widely used for predictions. For medical science, Machine learning and pattern recognition are various multidisciplinary research areas which provide effective techniques to diagnose ASD. The main aim of this paper is to analyze various Machine learning algorithms, used by various researcher like SVM (support Vector Machine), Random forest Scan, decision trees, logistic regression and compare the result based on their accuracy and efficiency.