Richardson AM, et al. – This work assessed the impact of three balancing methods and one feature selection method, to determine the ability of support vector machines (SVMs) to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections. Researchers realized that the balancing method, predictor variable selection and the virus type interact to affect the laboratory diagnosis of hepatitis virus infection with routine pathology laboratory variables in different ways depending on which combination is being studied. They here suggest laboratories looking to include machine learning via SVM as part of their decision support to be aware of this finding. This awareness should lead to careful use of existing machine learning methods, thus improving the quality of laboratory diagnosis.