Autism Prediction Using Deep Learning

Authors: Tharuni R, Tamilarasi S, Sathiya V, Subedha V, Sharmila S, Jabasheela L

Abstract: Healthcare is essential for human survival. The term "autism disease" encompasses a broad spectrum of symptoms utilized for diagnosis. Techniques for assessing diseases early on helped figure out the best way to handle high- risk people, which lowered their risk. The main goal is to keep people safe by spotting strange behavior. Researchers are working on ways to predict autism. Which disease can be diagnosed early? The model requires enhancement. In this paper, we propose a unified model which is hybrid of CNN and Bi-LSTM models to use deep learning methods to detect the presence of autism in individuals. We tackle the issues of missing and unbalanced data in the by employing data processing methods.

DOI: https://doi.org/10.5281/zenodo.19364751