Abstract
Ensemble learning aggregates a set of models to solve the same problem and usually gives better results than a single model. We apply the ensemble method to seek a better prediction in the Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge). We manage to obtain a much better predicting accuracy on the fluid intelligence with the proposed ensemble method using volumetric data from T1w brain image than a single prediction model. In addition, we compare the results of adolescents with young adults using data from the Human Connectome Project (HCP). We find that raw fluid intelligence scores in HCP without regressing out covariates such as age and brain volume can be much better predicted by brain structure. Also, the prediction, in general, is more accurate in young adults than adolescents.
H. Ren and X. Wang—Equally contribute to this paper.
Supported by the Health Sciences Center for Computational Innovation (HSCCI) at the University of Rochester.
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Ren, H., Wang, X., Wang, S., Zhang, Z. (2019). Predict Fluid Intelligence of Adolescent Using Ensemble Learning. 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_8
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DOI: https://doi.org/10.1007/978-3-030-31901-4_8
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