Among many psychiatric disorders, Autism Spectrum Disorder (ASD) affects people in diverse ways. Nowadays the prevalence of Autism spectrum disorder has increased gradually worldwide. Difficulty in social interaction, trouble with speech and nonverbal communication, repetitive actions, avoidance of eye contact and abnormal facial expressions are the primary symptoms of ASD. Predicting ASD at an early stage is important to provide necessary developmental support. Machine Learning algorithms play a vital role in prediction of ASD. In this study, Machine Learning algorithms like Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision trees, Random forest –CART and merging of Random Forest-CART and Random forest - Iterative Dichotomiser 3 (ID3) are compared for ASD prediction.

Autism Spectrum Disorder, Autistic Spectrum Disorders in Children and Adults, Machine Learning, Linear Discriminant Analysis, K-Nearest Neighbour, SVM, K-NN, Decision Trees, Random Forest CART & ID3

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