Receiver operating characteristics(ROC)curve and the area under the curve(AUC)value are often used to illustrate the diagnostic ability of binary classifiers.However,both ROC and AUC focus on high accuracy in theory,w...Receiver operating characteristics(ROC)curve and the area under the curve(AUC)value are often used to illustrate the diagnostic ability of binary classifiers.However,both ROC and AUC focus on high accuracy in theory,which may not be effective for practical applications.In addition,it is difficult to judge which one is better when the ROC curves are intersect and the AUC values are equal.Decision curve analysis(DCA)methods improve ROC by incorporating accuracy and consequences.However,similar to ROC,DCA requires a quantitative indicator to objectively determine which one is better when DCA curves intersect.A DCA-based statistical indicator named maximum net benefit(MNB)is constructed for evaluating clinical treatment regimens rather than just accuracy as in ROC and AUC.As a simple and effective statistical indicator,the construction process of MNB is given theoretically.Moreover,the MNB can still provide effective identification when the AUC values are equal,which is proved by theory.Furthermore,the feasibility and effectiveness of the proposed MNB are verified by gene selection and classifier performance comparison on actual data.展开更多
基金Support by Natural Science Foundation of Henan Province(Grant No.222300420417)Kaifeng Science and Technology Project(Grant No.2103004).
文摘Receiver operating characteristics(ROC)curve and the area under the curve(AUC)value are often used to illustrate the diagnostic ability of binary classifiers.However,both ROC and AUC focus on high accuracy in theory,which may not be effective for practical applications.In addition,it is difficult to judge which one is better when the ROC curves are intersect and the AUC values are equal.Decision curve analysis(DCA)methods improve ROC by incorporating accuracy and consequences.However,similar to ROC,DCA requires a quantitative indicator to objectively determine which one is better when DCA curves intersect.A DCA-based statistical indicator named maximum net benefit(MNB)is constructed for evaluating clinical treatment regimens rather than just accuracy as in ROC and AUC.As a simple and effective statistical indicator,the construction process of MNB is given theoretically.Moreover,the MNB can still provide effective identification when the AUC values are equal,which is proved by theory.Furthermore,the feasibility and effectiveness of the proposed MNB are verified by gene selection and classifier performance comparison on actual data.
