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

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1741
Author(s):  
Andra Ciocan ◽  
Nadim Al Hajjar ◽  
Florin Graur ◽  
Valentin C. Oprea ◽  
Răzvan A. Ciocan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Full Model ◽  
Classification Models ◽  
The Stability ◽  
Development And Validation ◽  
Receiver Operating

The stability of receiver operating characteristic in context of random split used in development and validation sets, as compared to the full models for three inflammatory ratios (neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte (dNLR) and platelet-to-lymphocyte (PLR) ratio) evaluated as predictors for metastasis in patients with colorectal cancer, was investigated. Data belonging to patients admitted with the diagnosis of colorectal cancer from January 2014 until September 2019 in a single hospital were used. There were 1688 patients eligible for the study, 418 in the metastatic stage. All investigated inflammatory ratios proved to be significant classification models on both the full models and on cross-validations (AUCs > 0.05). High variability of the cut-off values was observed in the unrestricted and restricted split (full models: 4.255 for NLR, 2.745 for dNLR and 255.56 for PLR; random splits: cut-off from 3.215 to 5.905 for NLR, from 2.625 to 3.575 for dNLR and from 134.67 to 335.9 for PLR), but with no effect on the models characteristics or performances. The investigated biomarkes proved limited value as predictors for metastasis (AUCs < 0.8), with largely sensitivity and specificity (from 33.3% to 79.2% for the full model and 29.1% to 82.7% in the restricted splits). Our results showed that a simple random split of observations, weighting or not the patients with and whithout metastasis, in a ROC analysis assures the performances similar to the full model, if at least 70% of the available population is included in the study.

scholarly journals Interaction of MRE11 and Clinicopathologic Characteristics in Recurrence of Breast Cancer: Individual and Cumulated Receiver Operating Characteristic Analyses

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Li-Yeh Chuang ◽  
Shyng-Shiou F. Yuan ◽  
Ming-Feng Hou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Growth Factor Receptor ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Prognostic Indicators ◽  
Clinicopathologic Characteristics ◽  
Receiver Operating

The interaction between the meiotic recombination 11 homolog A (MRE11) oncoprotein and breast cancer recurrence status remains unclear. The aim of this study was to assess the interaction between MRE11 and clinicopathologic variables in breast cancer. A dataset for 254 subjects with breast cancer (220 nonrecurrent and 34 recurrent) was used in individual and cumulated receiver operating characteristic (ROC) analyses of MRE11 and 12 clinicopathologic variables for predicting breast cancer recurrence. In individual ROC analysis, the area under curve (AUC) for each predictor of breast cancer recurrence was smaller than 0.7. In cumulated ROC analysis, however, the AUC value for each predictor improved. Ten relevant variables in breast cancer recurrence were used to find the optimal prognostic indicators. The presence of any six of the following ten variables had a high (79%) sensitivity and a high (70%) specificity for predicting breast cancer recurrence: tumor size ≥ 2.4 cm, tumor stage II/III, therapy other than hormone therapy, age ≥ 52 years, MRE11 positive cells > 50%, body mass index ≥ 24, lymph node metastasis, positivity for progesterone receptor, positivity for epidermal growth factor receptor, and negativity for estrogen receptor. In conclusion, this study revealed that these 10 clinicopathologic variables are the minimum discriminators needed for optimal discriminant effectiveness in predicting breast cancer recurrence.

scholarly journals Bayesian Sensitivity-Specificity and ROC Analysis for Finding Key Drivers

2021 ◽  
Vol 19 (1) ◽  
pp. 2-15
Author(s):  
Stan Lipovetsky ◽  
Michael W. Conklin
Keyword(s):  
Receiver Operating Characteristic ◽  
Statistical Modeling ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Regression Modeling ◽  
Modeling And Analysis ◽  
Numerical Comparisons ◽  
Key Drivers ◽  
Sensitivity Specificity ◽  
Receiver Operating

Finding key drivers in regression modeling via Bayesian Sensitivity-Specificity and Receiver Operating Characteristic is suggested, and clearly interpretable results are obtained. Numerical comparisons with other techniques show that this methodology can be useful in practical statistical modeling and analysis helping to researchers and managers in making meaningful decisions.

Receiver operating characteristic analysis of milk lactosefor identification of mastitis in buffaloes

2016 ◽  
Author(s):  
T. K. Patbandha ◽  
K. Ravikala ◽  
B. R. Maharana ◽  
Rupal Pathak ◽  
S. Marandi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Positive Likelihood Ratio ◽  
Threshold Values ◽  
Characteristic Analysis ◽  
Statistical Tool ◽  
Milk Samples ◽  
Diagnostic Indicator ◽  
Receiver Operating

Receiver operating characteristic (ROC) analysis is a simple statistical tool used to classify a diagnostic indicator in terms of area under a ROC curve (AUC) and to develop potential threshold values of a diagnostic indicator. Milk lactose was analyzed by ROC analysis to see its accuracy to discriminate infected and healthy udder quarters, and to develope an optimum threshold value along with corresponding sensitivity (Se), specificity (Sp) and positive likelihood ratio (LR+) value. Data for the present study comprised of 1516 milk samples collected from Jaffrabadi buffaloes. Milk lactose was estimated by milk analyzer ‘LACTOSCAN’ and further samples were checked for sub-clinical mastitis by California mastitis test (CMT). The threshold values of milk lactose for identification of moderate and severe infection were found to be 5.31g% (Se, 58.82%; Sp, 58.28%) and 5.23g% (Se, 70.97%; Sp, 64.41%), respectively by ROC analysis. Milk samples with lactose content below 5.31g% were 1.41 times more likely come from moderately infected quarters (LR+ = 1.41); whereas, below 5.23g% were 1.99 times more likely come from severely infected quarters (LR+ = 1.99). The overall accuracy of milk lactose for discrimination of normal quarters from moderately infected quarters was 64% (AUC=0.64) and from severely infected quarters was 72% (AUC=0.72) (P<0.001). Thus, the present study indicated that milk lactose classified mastitic and healthy udder quarters in Jaffrabadi buffaloes with moderate accuracy.

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