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While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic review update. Specifically, we used the soft-margin Support Vector Machine (SVM) as a classifier and examined various techniques to resolve class imbalance issues. Through an empirical study, we demonstrated that the soft-margin SVM works better than the perceptron algorithm used in current research and the performance of the classifier can be further improved by exploiting different sampling methods to resolve class imbalance issues.