Citation

Owida, Hamza Abu and Abu Alhaija, Mwaffaq and Vasudevan, Asokan and Mashagba, Hamza A. and Abd. Aziz, Azlan and Ahmmad Hunitie, Mohammad Faleh and Ab Aziz, Nor Azlina and Mohammad, Suleiman Ibrahim (2026) A Data Mining and Artificial Neural Network Approach for Autism Spectrum Disorder Detection. Applied Mathematics & Information Sciences, 20 (4). pp. 907-921. ISSN 1935-0090

Text 2.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

In this paper, we examine how deep learning—especially facial-image–based methods—shows promise for ASD detection. However, many models are limited by centralized training, which restricts the use of diverse, globally distributed datasets and can reduce performance across demographic groups. To address these limitations, we present a privacy-preserving federated learning framework that enables medical institutions worldwide to collaboratively train ASD detection models without sharing raw patient data beyond local sites. Our framework incorporates differential privacy, secure aggregation protocols, and adaptive communication strategies to support convergence and protect confidentiality under heterogeneous data distributions. Experiments using simulated data from five medical centers demonstrate that the federated model matches centralized baselines in predictive performance while improving demographic representation and reducing bias across ethnic and cultural groups. Across diverse populations, it achieves an average accuracy of 89.2%, precision of 88.7%, recall of 89.5%, and F1-score of 89.1%, underscoring the potential for fair, confidential global diagnostic technologies for neurodevelopmental disorders.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural networks, Data mining, Data processing, Big data analytics, Facial image analysis
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	31 Jul 2026 04:51
Last Modified:	31 Jul 2026 04:51
URII:	http://shdl.mmu.edu.my/id/eprint/16389

Downloads

Edit (login required)