Published Paper Details:

A neuro-disease known as autism spectrum disorder (ASD) affects a human's ability towards engage & communicate among others on a lifelong basis. Autism is referred towards as a "behavioural disorder" since symptoms typically develop in first two years about life, but it can be diagnosed at any point in one's life. According towards ASD theory, problems begin in childhood & persist into adolescence & maturity. This paper attempts towards investigate potential use about machine learning algorithms for predicting & analyzing ASD problems in children, adults & adolescents. On three separate publicly accessible, non-clinically relevant ASD datasets, suggested approaches are assessed. There are 292 instances & 21 attributes in first dataset relating towards screening for ASD in children. Adult individuals make up second dataset for ASD screening, which has a total about 704 instances & 21 attributes. There are 104 cases & 21 attributes in third dataset, which is focused on ASD screening in dolescent individuals. Convolutional neural network based models had higher accuracy about 99.53 percent, 98.30 percent, & 96.88 percent for the three datasets respectively. The results are after applying various machine learning techniques.