Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data

Abstract

In response to the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge 2019), we developed machine learning algorithms to predict the fluid intelligence (FI) score using T1-weighed magnetic resonance imaging (MRI) data. 122 volumetric scores of regions of interest from 3739 samples provided in the training set were used to train the models and 415 samples were assigned as validation samples. We performed feature reduction using principal factors factor analysis on the training set volume. 36 factors explaining 100% of the total variance were extracted; the top 18 explained 80% of the variance. We estimated three types of models based on: (1) all regional brain volumes, (2) the 18 top factors or (3) the 36 complete factors. We used Scikit-Learn’s grid search method to search the hyperparameter spaces of eight different machine learning algorithms. The best model, a Nu support vector regression model (NuSVR) using 36 factor scores as features, yielded the highest validation score (R² = 0.048) and a relatively low training score (0.22), the latter of which was important for reducing the degree of over-fitting. The mean squared errors (MSEs) for the training and validation samples were 68.2 and 68.6, respectively; the correlation coefficients were 0.54 and 0.21 (p < 0.0001 for both). The final MSE for the test set was 95.63. Learning curve analysis suggests that the current training sample size is still too small; increasing sample size should improve predictive accuracy. Overall, our results suggest that, given a large enough sample, machine learning methods with structural MRI data may be able to accurately estimate fluid intelligence.