Abstract
Prediction of cognitive performance from brain structural imaging data is a challenging machine learning topic. Participating in the ABCD Neurocognitive prediction challenge (2019), we implemented several machine learning models to solve this problem. Our results show superior performance from models relying on boosted decision trees and we find benefit from using two different sets of derived brain volumetric features. Lastly, across all models, we report an increase in performance by ensembling several different model types together in a final layer.
L. Brueggeman and T. Koomar—Denotes equal authorship.
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Brueggeman, L. et al. (2019). Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. In: Pohl, K., Thompson, W., Adeli, E., Linguraru, M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science(), vol 11791. Springer, Cham. https://doi.org/10.1007/978-3-030-31901-4_15
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