Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

ABSTRACT: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, existing basic model-based studies in ADHD report suboptimal classification performances and inconclusive results, mainly due to the limited flexibility for each type of basic classifier to appropriately handle multi-dimensional source features with varying properties. This study applied ensemble learning techniques (ELTs), a meta-algorithm that combine several basic machine learning models into one predictive model in order to decrease variance, bias, or improve prediction

SUBMITTER: Luo Y 

PROVIDER: S-EPMC7076568 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature