Bayesian model averaging over decision trees for assessing newborn brain maturity from electroencephalogram

Abstract

We use the Bayesian Model Averaging (BMA) over Decision Trees (DTs) for assessing newborn brain maturity from clinical EEG. We found that within this methodology an appreciable part of EEG features is rarely used in the DT models, because these features make weak contribution to the assessment. It was identified that the portion of DT models using weak EEG features is large. The negative impact of this is twofold. First, the use of weak features obstructs interpretation of DTs. Second, weak attributes increase dimensionality of a model parameter space needed to be explored in detail. We assumed that discarding the DTs using weak features will reduce the negative impact, and then proposed a new technique. This technique has been tested on some benchmark problems, and the results have shown that the original set of attributes can be reduced without a distinguishable decrease in BMA performance. On the EEG data, we found that the original set of features can be reduced from 36 to 12. Rerunning the BMA on the set of the 12 EEG features has slightly improved the performance.