Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019.

ABSTRACT: As we age, our brain structure changes and our cognitive capabilities decline. Although brain aging is universal, rates of brain aging differ markedly, which can be associated with pathological mechanism of psychiatric and neurological diseases. Predictive models have been applied to neuroimaging data to learn patterns associated with this variability and develop a neuroimaging biomarker of the brain condition. Aiming to stimulate the development of more accurate brain-age predictors, the Predictive Analytics Competition (PAC) 2019 provided a challenge that included a dataset of 2,640 participants. Here, we present our approach which placed between the top 10 of the challenge. We developed an ensemble of shallow machine learning methods (e.g., Support Vector Regression and Decision Tree-ba

SUBMITTER: Da Costa PF 

PROVIDER: S-EPMC7738323 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature