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Objective: This study illustrates the ambiguity of ROC in evaluating twoclassifiers of 90-day LVAD mortality. This paper also introduces the precisionrecall curve (PRC) as a supplemental metric that is more representative of LVADclassifiers performance in predicting the minority class.  Background: In the LVAD domain, the receiver operating characteristic (ROC)is a commonly applied metric of performance of classifiers. However, ROC canprovide a distorted view of classifiers ability to predict short-term mortalitydue to the overwhelmingly greater proportion of patients who survive, i.e.imbalanced data.  Methods: This study compared the ROC and PRC for the outcome of twoclassifiers for 90-day LVAD mortality for 800 patients (test group) recorded inINTERMACS who received a continuous-flow LVAD between 2006 and 2016 (mean ageof 59 years; 146 females vs. 654 males) in which mortality rate is only %8 at90-day (imbalanced data). The two classifiers were HeartMate Risk Score (HMRS)and a Random Forest (RF).  Results: The ROC indicates fairly good performance of RF and HRMS classifierswith Area Under Curves (AUC) of 0.77 vs. 0.63, respectively. This is incontrast with their PRC with AUC of 0.43 vs. 0.16 for RF and HRMS,respectively. The PRC for HRMS showed the precision rapidly dropped to only 10%with slightly increasing sensitivity.  Conclusion: The ROC can portray an overly-optimistic performance of aclassifier or risk score when applied to imbalanced data. The PRC providesbetter insight about the performance of a classifier by focusing on theminority class.

Limitations of ROC on Imbalanced Data: Evaluation of LVAD Mortality Risk Scores