Prediction patients with alcohol dependence via graph classification on Brain Network Derived from Functional Magnetic Resonance Imaging

Motivation: The study is motivated by the need for innovative approaches to alcoholism classification, leveraging neuro-functional network analysis from fMRI data to improve diagnostic accuracy and gain insights into alcoholism's complex nature. Goal(s): The primary goal is to achieve accurate alcoholism classification using functional connectivity patterns and machine learning. Approach: The study employed fMRI data from 15 healthy controls and 15 patients with alcohol dependence, utilizing advanced graph analysis techniques and machine learning algorithms. Results: The approach demonstrated a 73% classification accuracy, highlighting the potential of functional connectivity patterns as diagnostic markers and the value of machine learning in quantifying network differences. Impact: This research contributes to more precise alcoholism diagnosis and offers opportunities for biomarker discovery. It may facilitate earlier intervention and more effective treatments, benefiting both clinicians and patients. The impact includes advancing addiction research and improving patient care.