Early Detection Of ADHD Among Children Using Machine Learning

• Main Author: Nur Atiqah, Kamal
• Format: Undergraduates Project Papers
• Language: English
• Published: 2023
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/40905/1/CB20178.pdf; http://umpir.ump.edu.my/id/eprint/40905/

Early detection of attention-deficit/hyperactivity disorder (ADHD) in children is vital for timely intervention and improved outcomes. Functional magnetic resonance imaging (fMRI) has emerged as a valuable tool for understanding the neural basis of ADHD. This abstract explores the significance of early ADHD detection, the potential of fMRI for ADHD diagnosis, and the role of machine learning in facilitating early identification. By measuring brain activity patterns, fMRI provides insights into the functional abnormalities associated with ADHD. Machine learning algorithms can analyze fMRI data and identify biomarkers indicative of ADHD, enabling accurate classification. The integration of fMRI and machine learning offers a promising approach to early ADHD detection, allowing for personalized interventions and tailored treatment strategies. Early identification using fMRI and machine learning holds great potential for improving the lives of children with ADHD through timely interventions and targeted support.