Computer Aided Diagnosis System Based on Random Forests for the Prognosis of Alzheimer’s Disease

In this abstract, we propose an original CAD system consisting in the combination of
brain parcelling, ensemble of trees methods, and selection of (groups of) features using
the importance scores embedded in tree-based methods. Indeed, on top of their ease
of use and accuracy without ad hoc parameter tuning, tree ensemble methods such as
random forests (RF) (Breiman, 2001) or extremely randomized trees (ET) (Geurts et
al., 2006) provide interpretable results in the form of feature importance scores. We also
compare the performance and interpretability of our proposed method to standard RF
and ET approaches, without feature selection, and to multiple kernel learning (MKL). The latter was shown to be an efficient method notably capable of
dealing with anatomically defined regions of the brain by the use of multiple kernels.