International Research Journal of Education and Technology

Autism Spectrum Disorder (ASD) which is a neuro development disorder , is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetic in nature, early detection and treatment can help to improve the conditions .In recent years, machine learning based intelligent diagnosis has been evolved to complement the traditional clinical methods which can be time consuming and expensive. The focus of this paper is to classify the ASD and NON ASD and improve the accuracy using Tensor Flow 2(Keras) and Support Vector Machine (SVM) to obtain an accuracy of 99% from the our ASD and NON ASD datasets. The existing accuracy was 98.35 which we improved to 99% that gives better output.