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Limitations of receiver operating characteristic curve on imbalanced data: Assist device mortality risk scores

接收机工作特性 医学 随机森林 公制(单位) 心室辅助装置 曲线下面积 召回 精确性和召回率 F1得分 风险评估 人工智能 弗雷明翰风险评分 统计 机器学习 内科学 计算机科学 数学 心力衰竭 运营管理 哲学 语言学 经济 计算机安全 疾病
作者
Faezeh Movahedi,Rema Padman,James F. Antaki
出处
期刊:The Journal of Thoracic and Cardiovascular Surgery [American Association for Thoracic Surgery]
卷期号:165 (4): 1433-1442.e2 被引量:13
标识
DOI:10.1016/j.jtcvs.2021.07.041
摘要

In the left ventricular assist device domain, the receiver operating characteristic is a commonly applied metric of performance of classifiers. However, the receiver operating characteristic can provide a distorted view of classifiers' ability to predict short-term mortality due to the overwhelmingly greater proportion of patients who survive, that is, imbalanced data. This study illustrates the ambiguity of the receiver operating characteristic in evaluating 2 classifiers of 90-day left ventricular assist device mortality and introduces the precision recall curve as a supplemental metric that is more representative of left ventricular assist device classifiers in predicting the minority class.This study compared the receiver operating characteristic and precision recall curve for 2 classifiers for 90-day left ventricular assist device mortality, HeartMate Risk Score and Random Forest for 800 patients (test group) recorded in the Interagency Registry for Mechanically Assisted Circulatory Support who received a continuous-flow left ventricular assist device between 2006 and 2016 (mean age, 59 years; 146 female vs 654 male patients), in whom 90-day mortality rate is only 8%.The receiver operating characteristic indicates similar performance of Random Forest and HeartMate Risk Score classifiers with respect to area under the curve of 0.77 and Random Forest 0.63, respectively. This is in contrast to their precision recall curve with area under the curve of 0.43 versus 0.16 for Random Forest and HeartMate Risk Score, respectively. The precision recall curve for HeartMate Risk Score showed the precision rapidly decreased to only 10% with slightly increasing sensitivity.The receiver operating characteristic can portray an overly optimistic performance of a classifier or risk score when applied to imbalanced data. The precision recall curve provides better insight about the performance of a classifier by focusing on the minority class.
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