Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach
Open accessAbstract
ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum. This approach is very practical. In this study, the results of the proposed method are compared with those of the standard approaches, by using simulated data with different distribution and homogeneity conditions as well as a real data. According to the simulation results, the use of the proposed method is advised for finding the true cut-point.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The optimal oPMN cutoff value was 1.43 × 10^6/mL for both stage and grade.
“As listed in Table 3, the optimal oPMN cutoff value was determined to be 1.43 × 106/mL for both stage and grade as per the Index of Union (IU) method. 44”
From Prognostic utility of oral neutrophil counts in high-risk periodontitis: A retrospective study (2025) · cites this paper as [44]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · see citing works
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine
ZWEIG, M. H., & Campbell, G. (1993) · Clinical Chemistry · doi:10.1093/clinchem/39.4.561 · Cited by 6,528 · see citing works
Estimation of the Youden Index and its Associated Cutoff Point
Fluss, R., Faraggi, D., & Reiser, B. (2005) · Biometrical Journal · doi:10.1002/bimj.200410135 · Cited by 2,604 · see citing works
Perkins, N. J., & Schisterman, E. F. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj063 · Cited by 1,753 · see citing works
Schisterman, E. F., Perkins, N. J., Liu, A., & Bondell, H. D. (2004) · Epidemiology · doi:10.1097/01.ede.0000147512.81966.ba · Cited by 1,187 · see citing works
Statistical Methods in Diagnostic Medicine
Zhou, X., Obuchowski, N. A., & McClish, D. K. (2002) · Wiley series in probability and statistics · doi:10.1002/9780470317082 · Cited by 1,181 · see citing works
Classification accuracy and cut point selection
Liu, X. (2012) · Statistics in Medicine · doi:10.1002/sim.4509 · Cited by 679 · see citing works
Maximally Selected Chi Square Statistics
Miller, R. G., & Siegmund, D. (1982) · Biometrics · doi:10.2307/2529881 · Cited by 490 · see citing works
The Youden Index and the Optimal Cut‐Point Corrected for Measurement Error
Perkins, N. J., & Schisterman, E. F. (2005) · Biometrical Journal · doi:10.1002/bimj.200410133 · Cited by 289 · see citing works
Faraggi, D., & Simon, R. M. (1996) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19961030)15:20 · Cited by 145 · see citing works
Optimal Thresholds by Maximizing or Minimizing Various Metrics via ROC-Type Analysis
Zou, K. H., Yu, C., Liu, K., et al. (2013) · Academic Radiology · doi:10.1016/j.acra.2013.02.004 · Cited by 53 · see citing works
Finding the optimal cut-point for Gaussian and Gamma distributed biomarkers
Rota, M., & Antolini, L. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.07.015 · Cited by 48 · see citing works
Low pulse pressure as a predictor of death in patients with mild to advanced heart failure.
Yildiran, T., Koç, M., Bozkurt, A., Şahin, D. Y., Ünal, İ., & Acartürk, E. (2010) · PubMed Central · Cited by 37 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [3]Pepe M. S. The Statistical Evaluation of Medical Tests for Classification and Prediction 2003 28 Oxford, UK Oxford University Press Oxford Statistical Science Series MR2260483 Zbl1039.62105
Cited by
Sorted by citations
Large language models streamline automated machine learning for clinical studies
Tayebi Arasteh, S., Han, T., Lotfinia, M., Kuhl, C., Kather, J. N., Truhn, D., & Nebelung, S. (2024) · Nature Communications · doi:10.1038/s41467-024-45879-8 · Cited by 114 · see citing works
González-Robles, A., Mira, A., Miguel, C., Molinari, G., Díaz-García, A., García-Palacios, A., Bretón-López, J. M., Quero, S., Baños, R. M., & Botella, C. (2018) · PLOS ONE · doi:10.1371/journal.pone.0206516 · Cited by 51 · see citing works
Mira, A., González-Robles, A., Molinari, G., Miguel, C., Díaz-García, A., Bretón-López, J., García-Palacios, A., Quero, S., Baños, R., & Botella, C. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00180 · Cited by 35 · see citing works
Identifying standardized definitions of treatment response in trichotillomania: A meta-analysis
Farhat, L. C., Olfson, E., Li, F., Telang, S., & Bloch, M. H. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2018.10.009 · Cited by 32 · see citing works
AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview
Stylianides, C., Nicolaou, A., Sulaiman, W. A., Alexandropoulou, C.-A., Panagiotopoulos, I., Karathanasopoulou, K., Dimitrakopoulos, G., Kleanthous, S., Politi, E., Ntalaperas, D., et al. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7010006 · Cited by 28 · see citing works
Prognostic utility of oral neutrophil counts in high-risk periodontitis: A retrospective study
Prete, B. R. J., Barbour, A., Sun, C., Tenenbaum, H. C., Goldberg, M. B., & Glogauer, M. (2025) · Journal of Periodontology · doi:10.1002/jper.11360 · Cited by 5 · see citing works
- Authors
- Unal, I.
- Affiliations
- School of Medicine, Department of Biostatistics, Cukurova University, Saricam, Adana, Turkey
- ORCID
- Unal, Ilker
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2017
- Volume
- 2017
- Pages
- 1-14
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC5470053
- OpenAlex
- W2617595617
- Cited by
- 789
- References
- 15
- Citations by year
- 2026: 68 · 2025: 122 · 2024: 142 · 2023: 120 · 2022: 111 · 2021: 89 · 2020: 70 · 2019: 48 · 2018: 15 · 2017: 1
- FWCI
- 25.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- optimal cut-point, ROC curve, area under the ROC curve, diagnostic accuracy, biomarkers, sensitivity and specificity, threshold selection
- MeSH terms
- Computer Simulation, Humans, Models, Biological, ROC Curve, Sensitivity and Specificity, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
