Receiver Operating Characteristic Prediction for Classification: Performances in Cross-Validation by Example

Ciocan, Andra; Hajjar, Nadim Al; Graur, Florin; Oprea, Valentin; Ciocan, Răzvan A; Bolboacă, Sorana D.

doi:10.3390/math8101741

Cited by 7 publications

(4 citation statements)

References 70 publications

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“…The classification models using ROC analysis for NLR, dNLR and PLR were previously reported for the M stages [ 25 ]. However, we considered it worth investigating SII and PNI in correlation with the M stage, and these results are reported in the present study.…”

Section: Methodsmentioning

confidence: 99%

Abilities of Pre-Treatment Inflammation Ratios as Classification or Prediction Models for Patients with Colorectal Cancer

Ciocan

Hajjar

et al. 2021

Diagnostics

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Background: Systemic inflammatory status is known as an important factor of colorectal cancer prognosis. Our study aimed to evaluate the performances of inflammation biomarker ratios as classification models of seven outcomes in patients with colorectal cancer. Methods: A retrospective cohort study was conducted on subjects with colorectal cancer over five years at a single center in Transylvania, Romania. Seven derived ratios were calculated based on laboratory data: neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte (dNLR), platelet-to-lymphocyte (PLR), lymphocyte-to-monocyte (LMR) and albumin-to-globulin (AGR) ratios, Systemic Immune Inflammation Index (SII) and Prognostic Nutritional Index (PNI). The utility of these ratios as predictors for seven outcomes was further evaluated in multivariable regression models. Results: Our study shows that the evaluated ratios exhibit specific performances for individual outcomes, proving a fair ability as screening tools (NLR and dNLR for survival, T stage and M stage; NLR and SII for T stage; and PLR for M stage). A dNLR over 3.1 (OR = 2.48, 95% CI (1.421 to 4.331)) shows predictive value for survival. A value of NLR over 3.10 (OR = 1.389, 95% CI (1.061 to 1.817)) is positively associated with an advanced T stage, while LMR is negatively related to the T stage (OR = 0.919, 95% CI (0.867 to 0.975)). NLR over 4.25 (OR = 2.647, 95% CI (2.128 to 3.360)) is positively associated with, while PNI is negatively related (OR = 0.970, 95% CI (0.947 to 0.993)) to, the M stage. Conclusion: Each of the evaluated ratios possesses prognostic value for certain outcomes considered, but the reported models need external validation to recommend their clinical practice utilization.

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Section: Methodsmentioning

confidence: 99%

Abilities of Pre-Treatment Inflammation Ratios as Classification or Prediction Models for Patients with Colorectal Cancer

Ciocan

Hajjar

et al. 2021

Diagnostics

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“…However, the use of a statistical approach, as well as methods that are widely used in machine learning problems (for example, [22][23][24][25]) and radio engineering applications (for example, [20]), allow us to introduce the required criteria for evaluating the effectiveness of RPA. It is assumed that the values of the variables n c.t.…”

Section: Statistical Efficiency Indicators Of the Relay Protection Al...mentioning

confidence: 99%

Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches

et al. 2023

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The complication of the structure, topology and composition of the future electrical networks is characterized by difficult-to-recognize circuit-mode situations and requires modern methods for analyzing information parameters. The growing trend of digitizing signals in substations and the use of the IEC 61850 standard results in a huge amount of new data available at the nodes of the electrical network. The development and analysis of new methods for detecting and recognizing the modes of electrical networks (normal and emergency) are topical research issues. The article explores a new approach to recognizing a faulted section of an electrical network with branches by concurrently analyzing several information features and applying machine learning methods: decision tree, random forest, and gradient boosting. The application of this approach for decision-making by relay protection has not been previously implemented. Simulation modeling and the Monte Carlo method are at the heart of obtaining training samples. The results of testing the studied methods under review showed the required flexibility, the ability to use a large number of information parameters, as well as the best results of fault recognition in comparison with the distance protection relay.

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“…In contrast, cut-off point selection based on the heuristic rules shown here represents, at best, a rough estimate of an optimal cut-off point, although admittedly at moderately, and thereby practicable, sample sizes. However, the need for internal, temporal, and external validation of any chosen cut-off point remains [46,47].…”

Section: Transferability Of a Path-based Design From Early Phase Canc...mentioning

confidence: 99%

Convergence Behavior of Optimal Cut-Off Points Derived from Receiver Operating Characteristics Curve Analysis: A Simulation Study

Gerke

Zapf

2022

Mathematics

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The area under the receiver operating characteristics curve is a popular measure of the overall discriminatory power of a continuous variable used to indicate the presence of an outcome of interest, such as disease or disease progression. In clinical practice, the use of cut-off points as benchmark values for further treatment planning is greatly appreciated, despite the loss of information that such a dichotomization implies. Optimal cut-off points are often derived from fixed sample size studies, and the aim of this study was to investigate the convergence behavior of optimal cut-off points with increasing sample size and to explore a heuristic and path-based algorithm for cut-off point determination that targets stagnating cut-off point values. To this end, the closest-to-(0,1) criterion in receiver operating characteristics curve analysis was used, and the heuristic and path-based algorithm aimed at cut-off points that deviated less than 1% from the cut-off point of the previous iteration. Such a heuristic determination stopped after only a few iterations, thereby implicating practicable sample sizes; however, the result was, at best, a rough estimate of an optimal cut-off point that was unbiased and positively and negatively biased for a prevalence of 0.5, smaller than 0.5, and larger than 0.5, respectively.

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Receiver Operating Characteristic Prediction for Classification: Performances in Cross-Validation by Example

Cited by 7 publications

References 70 publications

Abilities of Pre-Treatment Inflammation Ratios as Classification or Prediction Models for Patients with Colorectal Cancer

Abilities of Pre-Treatment Inflammation Ratios as Classification or Prediction Models for Patients with Colorectal Cancer

Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches

Convergence Behavior of Optimal Cut-Off Points Derived from Receiver Operating Characteristics Curve Analysis: A Simulation Study

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