Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
This work addresses a domain-specific problem in neuroimaging for predicting cognitive abilities, but it is incremental as it applies an existing ensemble method to a new dataset.
The paper tackles predicting fluid intelligence in adolescents using T1-weighted MR images and a StackNet framework, achieving a mean squared error of 82.42 on a combined training/validation set and 94.25 on testing data.
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.