• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.103-109
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• 2015

In this paper, a novel method for the classification of term and preterm birth is proposed based on time-frequency analysis of electrohysterogram (EHG) using multivariate empirical mode decomposition (MEMD). EHG is a promising study for preterm birth prediction, because it is low-cost and accurate compared to other preterm birth prediction methods, such as tocodynamometry (TOCO). Previous studies on preterm birth prediction applied prefilterings based on Fourier analysis of an EHG, followed by feature extraction and classification, even though Fourier analysis is suboptimal to biomedical signals, such as EHG, because of its nonlinearity and nonstationarity. Therefore, the proposed method applies prefiltering based on MEMD instead of Fourier-based prefilters before extracting the sample entropy feature and classifying the term and preterm birth groups. For the evaluation, the Physionet term-preterm EHG database was used where the proposed method and Fourier prefiltering-based method were adopted for comparative study. The result showed that the area under curve (AUC) of the receiver operating characteristic (ROC) was increased by 0.0351 when MEMD was used instead of the Fourier-based prefilter.

• 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.

• Journal of Preventive Medicine and Public Health
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• v.32 no.3
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• pp.361-373
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• 1999

Objectives: Receive Operating Characteristic(ROC) curve with the area under the ROC curve(AUC) is one of the most popular indicator to evaluate the criterion validity of the measurement tool. This study was conducted to develop a standardized questionnaire to discriminate workers at high-risk of work-related musculoskeletal disorders using ROC analysis. Methods: The diagnostic results determined by rehabilitation medicine specialists in 370 persons(89 shipyard CAD workers, 113 telephone directory assistant operators, 79 women with occupation, and 89 housewives) were compared with participant's own replies to 'the questionnair on the worker's subjective physical symptoms'(Kwon, 1996). The AUC's from four models with different methods in item selection and weighting were compared with each other. These 4 models were applied to 225 persons, working in an assembly line of motor vehicle, for the purpose of AUC reliability test. Results: In a weighted model with 11 items, the AUC was 0.8155 in the primary study population, and 0.8026 in the secondary study population(p=0.3780). It was superior in the aspects of discriminability, reliability and convenience. A new questionnaire of musculoskeletal disorder could be constructed by this model. Conclusion: A more valid questionnaire with a small number of items and the quantitative weight scores useful for the relative comparisons are the main results of this study. While the absolute reference value applicable to the wide range of populations was not estimated, the basic intent of this study, developing a surveillance fool through quantitative validation of the measures, would serve for the systematic disease prevention activities.

• Journal of Veterinary Clinics
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• v.39 no.5
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• pp.199-206
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• 2022

Postpartum diseases should be predicted to prevent productivity loss before calving especially in organic dairy farms. This study was aimed to investigate the incidence of postpartum metabolic diseases in an organic dairy farm in Korea, to confirm the association between diseases and prepartum blood biochemical parameters, and to evaluate the accuracy of these parameters with a receiver operating characteristic (ROC) analysis for identifying vulnerable cows. Data were collected from 58 Holstein cows (16 primiparous and 42 multiparous) having calved for 2 years on an organic farm. During a transition period from 4 weeks prepartum to 4 weeks postpartum, blood biochemistry was performed through blood collection every 2 weeks with a physical examination. Thirty-one (53.4%) cows (9 primiparous and 22 multiparous) were diagnosed with at least one postpartum disease. Each incidence was 27.6% for subclinical ketosis, 22.4% for subclinical hypocalcemia, 12.1% for retained placenta, 10.3% for displaced abomasum and 5.2% for clinical ketosis. Between at least one disease and no disease, there were significant differences in the prepartum levels of parameters like body condition score (BCS), non-esterified fatty acid (NEFA), total bilirubin (T-bil), direct bilirubin (D-bil) and NEFA to total cholesterol (T-chol) ratio (p < 0.05). The ROC analysis of each of these prepartum parameters had the area under the curve (AUC) <0.7. However, the ROC analysis with logistic regression including all these parameters revealed a higher AUC (0.769), sensitivity (71.0%), and specificity (77.8%). The ROC analysis with logistic regression including the prepartum BCS, NEFA, T-bil, D-bil, and NEFA to T-chol ratio can be used to identify cows that are vulnerable to postpartum diseases with moderate accuracy.

• 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.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.691-701
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• 2017

The objective of this study is to evaluate the long-term drought outlook information based on long-term forecast data for the 2015 drought event. In order to estimate the Standardized Precipitation Index (SPI) for different durations (3-, 6-, 9-, 12-months), we used the observation precipitation of 59 Automated Synoptic Observing System (ASOS) sites, forecast and hindcast data of GloSea5. The Receiver Operating Characteristic (ROC) analysis and statistical analysis (Correlation Coefficient, CC; Root Mean Square Error, RMSE) were used to evaluate the utilization of drought outlook information for the forecast lead-times (1~6months). As a result of ROC analysis, ROC scores of SPI(3), SPI(6), SPI(9) and SPI(12) were estimated to be over 0.70 until the 2-, 3-, 4- and 5-months. The CC and RMSE values of SPI(3), SPI(6), SPI(9) and SPI(12) for forecast lead-time were estimated as (0.60, 0.87), (0.72, 0.95), (0.75, 0.95) and (0.77, 0.89) until the 2-, 4-, 5- and 6-months respectively.

• Journal of the Korean Society of Radiology
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• v.11 no.3
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• pp.131-137
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• 2017

This study investigated the risk factor of Gallbladder stone in Busan and Kyungnam area. The subjects of the experiment was performed with patients by abdominal ultrasonography among the patients who came to the P hospital from June 2016 to December 2016. Among them, risk factors were analyzed on 353 people at the same time when abdominal ultrasonography and hematological test. The statistical analysis of risk factors related to the Gallbladder stone was performed by independent t-test and chi-square test. In consider of difference verification result for calculations odds ratio about independent variables, multiple logistic regression analysis to conduct verify adequacy by calculating forecasting model from variable. As a result, Gallbladder stone risk factors have relevance to age ${\gamma}GTP$ with probability model and values to calculated. Age was showed sensitivity 49.7%, specificity 82.2%, receiver operating characteristic area under curve 0.724. Forecasting probability sensitivity 69.3%, specificity 62.4%, receiver operating characteristic area under curve 0.699 showed, ${\gamma}GTP$ confirmed validity of forecasting model.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2567-2570
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• 2013

Background: This study analyzed Surveillance, Epidemiology and End Results (SEER) data to assess if socio-economic factors (SEFs) impact on endometrial cancer survival. Materials and Methods: Endometrial cancer patients treated from 2004-2007 were included in this study. SEER cause specific survival (CSS) data were used as end points. The areas under the receiver operating characteristic (ROC) curve were computed for predictors. Time to event data were analyzed with Kaplan-Meier method. Univariate and multivariate analyses were used to identify independent risk factors. Results: This study included 64,710 patients. The mean follow up time (S.D.) was 28.2 (20.8) months. SEER staging (ROC area of 0.81) was the best pretreatment predictor of CSS. Histology, grade, race/ethnicity and county level family income were also significant pretreatment predictors. African American race and low income neighborhoods decreased the CSS by 20% and 3% respectively at 5 years. Conclusions: This study has found significant endometrial survival disparities due to SEFs. Future studies should focus on eliminating socio-economic barriers to good outcomes.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.307-319
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• 2018

High-throughput technologies enable the simultaneous evaluation of thousands of genes that could discriminate different subclasses of complex diseases. Ranking genes according to differential expression is an important screening step for follow-up analysis. Many statistical measures have been proposed for this purpose. A good ranked list should provide a stable rank (at least for top-ranked gene), and the top ranked genes should have a high power in differentiating different disease status. However, there is a lack of emphasis in the literature on ranking genes based on these two criteria simultaneously. To achieve the above two criteria simultaneously, we proposed to apply a previously reported metric, the modified area under the receiver operating characteristic cure, to gene ranking. The proposed ranking method is found to be promising in leading to a stable ranking list and good prediction performances of top ranked genes. The findings are illustrated through studies on both synthesized data and real microarray gene expression data. The proposed method is recommended for ranking genes or other biomarkers for high-dimensional omics studies.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1489-1502
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• 2019

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.