  In this work, we utilize T1-weighted MR images and StackNet to predict fluid
intelligence in adolescents. Our framework includes feature extraction, feature
normalization, feature denoising, feature selection, training a StackNet, and
predicting fluid intelligence. The extracted feature is the distribution of
different brain tissues in different brain parcellation regions. The proposed
StackNet consists of three layers and 11 models. Each layer uses the
predictions from all previous layers including the input layer. The proposed
StackNet is tested on a public benchmark Adolescent Brain Cognitive Development
Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of
82.42 on the combined training and validation set with 10-fold
cross-validation. In addition, the proposed StackNet also achieves a mean
squared error of 94.25 on the testing data. The source code is available on
GitHub.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning