  In medical domain, data features often contain missing values. This can
create serious bias in the predictive modeling. Typical standard data mining
methods often produce poor performance measures. In this paper, we propose a
new method to simultaneously classify large datasets and reduce the effects of
missing values. The proposed method is based on a multilevel framework of the
cost-sensitive SVM and the expected maximization imputation method for missing
values, which relies on iterated regression analyses. We compare classification
results of multilevel SVM-based algorithms on public benchmark datasets with
imbalanced classes and missing values as well as real data in health
applications, and show that our multilevel SVM-based method produces fast, and
more accurate and robust classification results.
