Development of an autism screening classification model for toddlers

TL;DR

This paper develops machine learning classifiers, including neural networks, to improve early autism screening in toddlers, aiming to facilitate timely diagnosis and intervention.

Contribution

It compares three machine learning models for ASD screening, highlighting the neural network's superior performance using a specific dataset.

Findings

Neural Network outperformed Decision Tree and Random Forest.

All models achieved high accuracy in ASD detection.

The study supports machine learning as a tool for early autism screening.

Abstract

Autism spectrum disorder ASD is a neurodevelopmental disorder associated with challenges in communication, social interaction, and repetitive behaviors. Getting a clear diagnosis for a child is necessary for starting early intervention and having access to therapy services. However, there are many barriers that hinder the screening of these kids for autism at an early stage which might delay further the access to therapeutic interventions. One promising direction for improving the efficiency and accuracy of ASD detection in toddlers is the use of machine learning techniques to build classifiers that serve the purpose. This paper contributes to this area and uses the data developed by Dr. Fadi Fayez Thabtah to train and test various machine learning classifiers for the early ASD screening. Based on various attributes, three models have been trained and compared which are Decision tree C4.5, Random Forest, and Neural Network. The three models provided very good accuracies based on testing data, however, it is the Neural Network that outperformed the other two models. This work contributes to the early screening of toddlers by helping identify those who have ASD traits and should pursue formal clinical diagnosis.