  Structural magnetic resonance imaging studies have shown that brain
anatomical abnormalities are associated with cognitive deficits in preterm
infants. Brain maturation and geometric features can be used with machine
learning models for predicting later neurodevelopmental deficits. However,
traditional machine learning models would suffer from a large
feature-to-instance ratio (i.e., a large number of features but a small number
of instances/samples). Ensemble learning is a paradigm that strategically
generates and integrates a library of machine learning classifiers and has been
successfully used on a wide variety of predictive modeling problems to boost
model performance. Attribute (i.e., feature) bagging method is the most
commonly used feature partitioning scheme, which randomly and repeatedly draws
feature subsets from the entire feature set. Although attribute bagging method
can effectively reduce feature dimensionality to handle the large
feature-to-instance ratio, it lacks consideration of domain knowledge and
latent relationship among features. In this study, we proposed a novel
Ontology-guided Attribute Partitioning (OAP) method to better draw feature
subsets by considering the domain-specific relationship among features. With
the better partitioned feature subsets, we developed an ensemble learning
framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied
the OAP-EL to predict cognitive deficits at 2 years of age using quantitative
brain maturation and geometric features obtained at term equivalent age in very
preterm infants. We demonstrated that the proposed OAP-EL approach
significantly outperformed the peer ensemble learning and traditional machine
learning approaches.
