Related Experiment Video
Updated: May 12, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
Published on: August 16, 2024
A comparison of two diagnostic performance measures: signal-to-noise ratio versus partial area under receiver
Mehmet Tolga Taner1, Bulent Sezen, Kamal Atwat
1Department of Healthcare Management, Uskudar University, Istanbul, Turkey.
Purpose:
This paper aims to compare two diagnostic performance measures, i.e. signal-to-noise ratio (S/N ratio) and partial area under receiver operating characteristic curves (pAUC). It proposes the use of S/N ratio rather than pAUC for establishing optimal cut-off point for diagnostic biomarkers.
Design/Methodology/Approach:
This paper discusses the properties, uses, advantages and shortcomings of the two performance measures, namely the partial area under receiver operating characteristic curve (pAUC) and Taguchi's signal-to-noise (S/N) ratio. The benefits of S/N ratio have been illustrated in a sample of four biomarkers, each having five cut-off points. The S/N ratio is compared to the pAUC index. The SAS software is employed to calculate pAUC and AUC.
Findings:
This paper shows that S/N ratio can be used as a measure of diagnostic accuracy. The cut-off point with the highest S/N ratio is the optimal cut-off point for the biomarker. The proposed method has the advantages of being easier, more practical and less costly than that of pAUC.
Practical Implications:
This paper includes implications for the development of a more practical, equally powerful and less costly means of measuring clinical accuracy thereby reducing the costs and risks resulting from wrong selection of cut-off point can be decreased.
Originality/Value:
This paper supports suggestions in the recent literature to replace pAUC with a new, more meaningful index.
Related Concept Videos
Receiver Operating Characteristic Plot
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Odds Ratio
Comparing the Survival Analysis of Two or More Groups
Calibration Curves: Correlation Coefficient
Region of Convergence of Laplace Tarnsform
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...