Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction

Predicting fluid intelligence based on T1-weighted magnetic resonance imaging (MRI) scans poses several challenges, including developing an adequate data representation of three dimensional voxel data, extracting predictive information from this data representation, and devising a model that is able to leverage the predictive information. We evaluate two strategies for prediction of fluid intelligence given structural MRI scans acquired through the Adolescent Brain Cognitive Development (ABCD) Study: deep learning models trained on raw imagery and classical machine learning models trained on extracted features. Our best-performing solution consists of a classical machine learning model trained on a combination of provided brain volume estimates and extracted features. Specifically, a Gradient Boosting Regressor (GBR) trained on a PCA-reduced feature space produced the best performance (train MSE = 66.29, validation MSE = 70.16), surpassing regression models trained on the provided volume data alone, and 2D/3D Convolutional Neural Networks trained on various representations of imagery data. Nonetheless, these results remain slightly better than a mean prediction, suggesting that neither approach is capturing a high degree of variance in the data.