• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• Imaging Science in Dentistry
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• v.30 no.3
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• pp.155-158
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• 2000

The purpose of this paper is to explain the making procedure and the usage of receiver operating characteristic (ROC) curve for interpretation of radiographic images. The conventional radiograms obtained after the creation of the lesions in the acrylic plates and were enhanced in color. The observer were informed of which tooth to examine, the 'a priori' probability of a lesion present and the approximate diameter of the lesions. The two groups of films were interpreted separately by the same observer using the same rating scale. The following rating scale was used: A; definitely no lesion, B; probably no lesion, C; not sure, D; probably a lesion, and E; definitely a lesion. In analysis, for each observer the diagnostic results in terms of true positive (TP) and false positive (FP) decisions were plotted on a graph. The lowest point on the graph represents the TP and FP when only decisions designated as E according to the rating scale are included. The next point shows the TP and FP values when diagnoses designated as D are added and so forth. By connecting such plot points, a receiver operating characteristic (ROC) curves is obtained. The area under the curve represents the diagnostic accuracy resulting from a diagnostic performance at pure chance level and a value of 1.0 at perfect performance. This method has been known as an useful method to detect the minute difference for each radiographic technic, each observer and for the different lesion depths.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.910-924
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• 2010

As Internet usage has increased, the risk of adolescents being exposed to adult content and harmful information on the Internet has also risen. To help prevent adolescents accessing this content, a novel detection method for adult images is proposed. The proposed method involves three steps. First, the Image Of Interest (IOI) is extracted from the image background. Second, the IOI is distinguished from the segmented image using a novel weighting mask, and it is determined to be acceptable or unacceptable. Finally, the features (color and texture) of the IOI or original image are compared to a critical value; if they exceed that value then the image is deemed to be an adult image. A Receiver Operating Characteristic (ROC) curve analysis was performed to define this optimal critical value. And, the textural features are identified using a gray level co-occurrence matrix. The proposed method increased the precision level of detection by applying a novel weighting mask and a receiver operating characteristic curve. To demonstrate the effectiveness of the proposed method, 2850 adult and non-adult images were used for experimentation.

• The korean journal of orthodontics
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• v.25 no.2 s.49
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• pp.153-163
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• 1995

In this study, Receiver Operating Characteristic(ROC) analysis was used to evaluate the ability of cephalometric measurements to identify patients with Class III malocclusions. ROC analysis is the method for determining the validity of a diagnostic measure and for evaluating the relative value of diagnostic tests. The sample consisted of 496 patients with malocclusion. Class III malocclusion is defined as the dental relationship for which The mesiobuccal groove of the lower first molar is deviated mesially from the mesiobuccal cusp of the upper first molar. Of the total sample of 496 patients, 245 had Class III malocclusions. 16 cephalometric measurements were selected, each of which was treated as a diagnostic test. The ROC curves were generated for each cephalometric measurement with intervals of $1.0^{\circ}$ for angular measurements, 1.0mm for linear measurements. The area under the ROC curves was measured for direct comparison among different diagnostic tests. The results were as follows; 1. The 'Wits' appraisal was found to be a better diagnostic criterion for the presence of Class III malocclusion than any other commonly'used cephalometric measurement. 2. AB plane angle, ANB angle, App-Bpp distance, AF-BF distance, APDI, Distance of point A and Pog to N perpendicular, maxillomandibular differential had high diagnostic value. 3. Cephalometric measurements which evaluate the position of the mandible had moderate diagnostic value. 4. Cephalometric measurements related to the maxilla discriminated least between patients with and without Class III malocclusion.

• Physical Therapy Rehabilitation Science
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• v.7 no.2
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• pp.54-60
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• 2018

Objective: Persons with chronic stroke fall more often than healthy elderly individuals. The Timed Up and Go test (TUG) is used as a fall prediction tool, but only provides a result for the total measurement time. This study aimed to determine the optimal cut-off values for each of the 6 components of the TUG. Design: Retrospective study. Methods: Thirty persons with chronic stroke participated in the study. TUG evaluation was performed using a wearable miniaturized inertial sensor. Sensitivity, specificity, and predictive values were calculated using the Receiver Operating Characteristic (ROC) curve analysis for the measured values in each section. Optimal values for fall risk classification were determined. Logistic regression analysis was used to investigate the risk of future falls based on TUG. Results: The cut-off values of the 6 sections of the TUG were determined, as follows: sit-to-stand >2.00 seconds (p<0.05), forward gait >4.68 seconds (p<0.05), mid-turn >3.82 seconds (p<0.05), return gait >4.81 seconds (p<0.05), end-turn >2.95 seconds (p<0.05), and stand-to-sit >2.13 seconds (p<0.05). The risk of falling increased by 2.278 times when the mid-turn value was >3.82 seconds (p<0.05). Conclusions: The risk of falls increased by 2.28 times when the value of the mid-turn interval exceeded 3.82 seconds. Therefore, when interpreting TUG results, the predictive accuracy for falls will be higher when the measurement time for each section is analyzed, together with the total time for TUG.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.275-292
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• 2010

Receiver operating characteristic(ROC) curves can be used to assess the accuracy of tests measured on ordinal or continuous scales. The most commonly used measure for the overall diagnostic accuracy of diagnostic tests is the area under the ROC curve(AUC). When two ROC curves are constructed based on two tests performed on the same individuals, statistical analysis on differences between AUCs must take into account the correlated nature of the data. This article focuses on confidence interval estimation of the difference between paired AUCs. We compare nonparametric, maximum likelihood, bootstrap and generalized pivotal quantity methods, and conduct a monte carlo simulation to investigate the probability coverage and expected length of the four methods.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.353-356
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• 2016

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) for glassy cell carcinoma data to identify predictive models and potential disparities in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors. For risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. Area under the receiver operating characteristic curves (ROCs) were computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate modeling errors. Risk of glassy cell carcinoma death was computed for the predictors for comparison. Results: There were 79 patients included in this study. The mean follow up time (S.D.) was 37 (32.8) months. Female patients outnumbered males 4:1. The mean (S.D.) age was 54.4 (19.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.69). The risks of cause specific death were, respectively, 9.4% for localized, 16.7% for regional, 35% for the un-staged/others category, and 60% for distant disease. After optimization, separation between the regional and unstaged/others category was removed with a higher ROC area of 0.72. Several socio-economic factors had small but measurable effects on outcome. Radiotherapy had not been used in 90% of patients with regional disease. Conclusions: Optimized SEER stage was predictive and useful in treatment selection. Underuse of radiotherapy may have contributed to poor outcome.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4587-4592
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• 2012

Purpose: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) neuroblastoma (NB) and other peripheral nerve cell tumors (PNCT) outcome data. This study found under usage of radiotherapy in these patients. Materials and methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for NB and other PNCT. For the risk modeling, each factor was fitted by a generalized linear model to predict the outcome (soft tissue specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate the modeling errors. Risk of neuroendocrine (other endocrine including thymus as coded in SEER) death was computed for the predictors. Results: There were 5261 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 83.8 (97.6) months. The mean (SD) age was 18 (25) years. About 30.45% of patients were un-staged. The SEER staging has high ROC (SD) area of 0.58 (0.01) among the factors tested. We simplified the 4-layered risk levels (local, regional, distant, un-staged/others) to a simpler 3-tiered model with comparable ROC area of 0.59 (0.01). Less than 50% of PNCT patients received radiotherapy (RT) including the ones with localized disease. This avoidance of RT use occurred in adults and children. Conclusion: The high under-staging rate may have precented patients from selecting definitive radiotherapy (RT) after surgery. Using RT for, especially, adult PNCT patients is a potential way to improve outcome.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.146-155
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• 2020

This study was to evaluate the usefulness of gamma glutamyl transferase (GGT) as a surrogate marker predicting metabolic syndrome. 7,155 non obese men over the age of 20 were studied as subjects. The criteria for diagnosing MetS were the National Cholesterol Education Program - Third Adult Treatment Panel (NCEP-ATP III). The risk of developing MetS according to GGT was conducted logistic regression analysis, and the ROC (receiver operating characteristic) curve was obtained to confirm GGT ability to predict the risk of MetS. Regardless of age and body mass index, MetS had a 7.09 times higher risk of onset in the fourth quartile than in the first quartile of GGT (p<0.001). The AUC (area under the curve) of GGT for the diagnosis of MetS was 0.715, and the cutoff value of GGT was 40.0 U/L, the sensitivity was 65.0%, and the specificity was 70.2%. Therefore, GGT is considered to be a useful diagnostic index for diagnosing MetS.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1803-1806
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• 2013

Purpose: Carbohydrate antigen (CA) 242 is inversely related to prognosis in many cancers. However, few data regarding CA 242 in esophageal cancer (EC) are available. The aim of this study was to determine the prognostic value of CA 242 and propose an optimum cut-off point in predicting survival difference in patients with esophageal squamous cell carcinoma (ESCC). Methods: A retrospective analysis was conducted of 192 cases. A receiver operating characteristic (ROC) curve for survival prediction was plotted to verify the optimum cuf-off point. Univariate and multivariate analyses were performed to evaluate prognostic parameters for survival. Results: The positive rate for CA 242 was 7.3% (14/192). The ROC curve for survival prediction gave an optimum cut-off of 2.15 (U/ml). Patients with CA 242 ${\leq}$ 2.15 U/ml had significantly better 5-year survival than patients with CA 242 >2.15 U/ml (45.4% versus 22.6%; P=0.003). Multivariate analysis showed that differentiation (P=0.033), CA 242 (P=0.017), T grade (P=0.004) and N staging (P<0.001) were independent prognostic factors. Conclusions: Preoperative CA 242 is a predictive factor for long-term survival in ESCC, especially in nodal-negative patients. We conclude that 2.15 U/ml may be the optimum cuf-off point for CA 242 in predicting survival in ESCC.