  This study evaluates the performance of various supervised machine learning
models in analyzing highly correlated neural signaling data from the Adolescent
Brain Cognitive Development (ABCD) Study, with a focus on predicting
obsessive-compulsive disorder scales. We simulated a dataset to mimic the
correlation structures commonly found in imaging data and evaluated logistic
regression, elastic networks, random forests, and XGBoost on their ability to
handle multicollinearity and accurately identify predictive features. Our study
aims to guide the selection of appropriate machine learning methods for
processing neuroimaging data, highlighting models that best capture underlying
signals in high feature correlations and prioritize clinically relevant
features associated with Obsessive-Compulsive Disorder (OCD).
