Using ensemble of Bayesian classifying algorithms for medical systematic reviews

Abstract

Systematic reviews are considered fundamental tools for Evidence-Based Medicine. Such reviews require frequent and time- consuming updating. This study aims to compare the performance of combining relatively simple Bayesian classifiers using a fixed rule, to the relatively complex linear Support Vector Machine for medical systematic reviews. A collection of four systematic drug reviews is used to compare the performance of the classifiers in this study. Cross-validation experiments were performed to evaluate performance. We found that combining Discriminative Multinomial Naïve Bayes and Complement Naïve Bayes performs equally well or better than SVM while being about 25% faster than SVM in training time. The results support the usefulness of using an ensemble of Bayesian classifiers for machine learning-based automation of systematic reviews of medical topics, especially when datasets have a large number of abstracts. Further work is needed to integrate the powerful features of such Bayesian classifiers together. © 2014 Springer International Publishing.