Machine Learning Model To Predict Autism Investigating Eye-Tracking Dataset

Abstract

Autism spectrum disorder is a neurodevelopmental disorder that characterizes by reducing concentration on social activities and improving interest in non-social tasks. The aim of this work is to investigate eye gazing images and identify autism applying various machine learning techniques. Therefore, we collected eye-tracking data from the Figshare data repository. But, these scanpath images were almost similar for normal and autistic children. To obtain similar groups, k-means clustering method was used and generated four clusters. Further, several classifiers were applied into primary data and these clusters and evaluated the performance of them using various metrics. After the assessment of overall results, MLP shows the highest 87% accuracy in cluster 1. In addition, it shows the best area under curve, f-measure, g-mean, sensitivity, specificity, fall out and miss rate respectively. This predictive model could notably useful to forecast ASD status at early stages.