# Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet

## TL;DR

This paper presents a novel StackNet-based framework utilizing T1-weighted MR images to predict fluid intelligence in adolescents, demonstrating competitive accuracy on a public benchmark dataset.

## Contribution

The study introduces a multi-layer StackNet model that integrates predictions from previous layers for improved fluid intelligence prediction from brain imaging data.

## Key findings

- Achieved a mean squared error of 82.42 on validation data
- Achieved a mean squared error of 94.25 on testing data
- Demonstrated effectiveness on a public neurocognitive prediction challenge

## Abstract

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.