Date of Award
12-2024
Document Type
Project
Degree Name
Master of Science in Computer Science
Department
School of Computer Science and Engineering
First Reader/Committee Chair
Jennifer Jin
Abstract
Autism Spectrum Disorder (ASD) diagnosis requires an integrative approach that combines behavioral, biomedical, and computational methodologies for enhanced accuracy. This study introduces a comprehensive framework that employs machine learning (ML) and deep learning (DL) techniques alongside linear regression to model relationships between behavioral traits, biomedical markers, and ASD likelihood. Behavioral inputs, such as social interaction patterns, repetitive behaviors, and communication characteristics, are analyzed using linear regression to identify significant predictors of ASD. Simultaneously, a Convolutional Neural Network (CNN) is trained on image datasets to detect visual cues, such as facial expressions, associated with ASD. Advanced techniques, including transfer learning and fine-tuning, are utilized to enhance the CNN's performance. By combining statistical and deep learning methods, this approach aims to deliver a robust and scalable solution for ASD diagnosis, promoting early detection and tailored interventions.
Recommended Citation
Jeevarathinam, Vidhya Lakshmi, "AUTISM SPECTRUM DISORDER" (2024). Electronic Theses, Projects, and Dissertations. 2103.
https://scholarworks.lib.csusb.edu/etd/2103