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This work is motivated by the needs of predictive analytics on healthcaredata as represented by Electronic Medical Records. Such data is invariablyproblematic: noisy, with missing entries, with imbalance in classes ofinterests, leading to serious bias in predictive modeling. Since standard datamining methods often produce poor performance measures, we argue fordevelopment of specialized techniques of data-preprocessing and classification.In this paper, we propose a new method to simultaneously classify largedatasets and reduce the effects of missing values. It is based on a multilevelframework of the cost-sensitive SVM and the expected maximization imputationmethod for missing values, which relies on iterated regression analyses. Wecompare classification results of multilevel SVM-based algorithms on publicbenchmark datasets with imbalanced classes and missing values as well as realdata in health applications, and show that our multilevel SVM-based methodproduces fast, and more accurate and robust classification results.

Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values