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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1489463

Data-Centric Automated Approach to Predict Autism Spectrum Disorder Based on Selective Features and Explainable Artificial Intelligence

Provisionally accepted
Asma Aldrees Asma Aldrees 1Stephen Ojo Stephen Ojo 2*James Wanliss James Wanliss 2Muhammad Umer Muhammad Umer 3Muhammad Attique Khan Muhammad Attique Khan 4Bayan Alabdullah Bayan Alabdullah 5Nisreen Innab Nisreen Innab 6*
  • 1 Departement of Compuetr Engineering, College of Computer Science, King Khalid Universityty, Abha, Saudi Arabia
  • 2 Anderson University, Anderson, Indiana, United States
  • 3 Islamia University of Bahawalpur, Bahawalpur, Pakistan
  • 4 Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
  • 5 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia, Riyadh, Riyadh, Saudi Arabia
  • 6 Department of Computer Science and Information Systems, College of Applied Sciences, University of Almaarefa, Dariyah, Riyadh, Saudi Arabia

The final, formatted version of the article will be published soon.

    Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this

    Keywords: Autism Spectrum Disorder, data-centric analysis, Feature engineering, multi-model learning Frontiers, XGBoost (Extreme Gradient Boosting)

    Received: 03 Sep 2024; Accepted: 01 Oct 2024.

    Copyright: © 2024 Aldrees, Ojo, Wanliss, Umer, Khan, Alabdullah and Innab. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Stephen Ojo, Anderson University, Anderson, Indiana, United States
    Nisreen Innab, Department of Computer Science and Information Systems, College of Applied Sciences, University of Almaarefa, Dariyah, 71666, Riyadh, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.