Small area estimation of receiver operating characteristic curves for ordinal data under stochastic ordering

Jang, Eun Jin; Nandram, Balgobin; Ko, Yousun; Kim, Dal Ho

doi:10.1002/sim.8493

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…The receiver operating characteristic (ROC) curve is also known as the sensitivity curve [19]- [22]. It can easily detect the ability to recognize performance at any threshold and select the best diagnostic threshold [23]- [26].…”

Section: Divide Risk Levels Based On Roc Curvementioning

confidence: 99%

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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This article provides a method based on self-organizing maps (SOM) neural network clustering and support vector machine (SVM) ensembles to predict the survival risk levels of esophageal cancer. Nine blood indexes related to patient survival are found by using SOM clustering method. Two critical thresholds for survival are found by plotting the receiver operating characteristic (ROC) curve twice, and the lifetime is divided into three risk levels. Using the SVM method, patients' risk levels are predicted and assessed. Four kernel functions of SVM are compared, and the prediction effect of RBF kernel function is better than other kernel functions. The parameters of SVM are optimized by using genetic algorithm (GA), particle swarm algorithm (PSO) and artificial bee colony (ABC) algorithm. Experimental results show that the prediction accuracies are improved by using optimization algorithms. After comparison, ABC-SVM has better prediction results than GA-SVM and PSO-SVM with a high prediction rate and fast running time. INDEX TERMS artificial bee colony, genetic algorithm, particle swarm optimization, self-organizing maps, support vector machine.

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Section: Divide Risk Levels Based On Roc Curvementioning

confidence: 99%

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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“…The discrimination capability of the nomogram was assessed using the receiver operating characteristic (ROC) curve, which is equivalent to the concordance statistic [ 6 ]. The accuracy of the predictive model was presented as the area under the ROC curve [ 7 ], and a higher value indicates better outcomes. A calibration curve was used to evaluate the model.…”

Section: Methodsmentioning

confidence: 99%

Clinicopathological Characteristics and Prognosis of Nasopharyngeal Lymphoepithelial Carcinoma: A Population-Based Retrospective Study

et al. 2020

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Background The aim of this study was to establish a predictive model for prognostic factors and overall survival (OS) in nasopharyngeal lymphoepithelial carcinoma (NLEC) patients. Material/Methods The data of 538 NLEC patients diagnosed between 1988 and 2015 were extracted from the Surveillance, Epidemiology, and End Results database. Patients who were diagnosed from 1988 to 1999 were included in the validation cohort, and those diagnosed from 2000 to 2015 in the primary cohort. Least absolute shrinkage and selection operator and multivariate Cox regression analyses were performed. The discrimination and calibration capabilities of the predictive models were evaluated using the receiver operating characteristic (ROC) curve and calibration plot, respectively. Results Radiotherapy ( P <0.0001), early-stage cancer based on the American Joint Committee on Cancer (AJCC) staging system ( P <0.0001), younger age ( P =0.0005) were associated with better OS rates. In the primary cohort, the areas under the ROC curves (AUC) of the nomogram for predicting 1-, 10-, and 15-year OS were 0.749, 0.754, and 0.81, respectively. Meanwhile, in the validation cohort, the AUC of the nomogram for predicting 1-, 10-, and 15-year OS were 0.692, 0.692, and 0.682, respectively. Furthermore, the calibration plot exhibited optimal agreements between the nomogram-predicted and actual 1-, 10-, and 15-year OS in both cohorts. The 1-, 10-, and 15-year OS rates were 93.6%, 62.7%, and 49.9%, respectively. Conclusions Age, early-stage cancer based on the AJCC staging system, radiotherapy, and gender can be used to predict OS in nasopharyngeal lymphoepithelial carcinoma patients.

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“…At the same time, the SIMPLE algorithm [22][23][24][25] is used and it takes the pressure as the basic variable. ere are five steps in the process of the SIMPLE algorithm, shown in Figure 4.…”

Section: Working Principle Of Flowmetermentioning

confidence: 99%

Application of Characteristic Model-Based Principal Component Analysis in Optimization of Flowmeter Parameters

Jiang

Wang

et al. 2021

Mathematical Problems in Engineering

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In complex industrial processes, it is necessary to perform modeling analysis on some industrial systems and find and optimize the factors that have the greatest impact on the results, in order to achieve the optimization of the industrial systems. However, due to the high-level nature or complex working mechanism of complex industrial systems, traditional principal component analysis methods are difficult to apply. Therefore, this paper proposes a characteristic model-based principal component analysis (CMPCA) to perform principal component analysis on complex industrial systems. The differential pressure flowmeter is taken as an example to verify the effectiveness of the method. Flowmeter is an indispensable instrument in measurement, and its accuracy depends on its own structural parameters. However, the measurement accuracy of some flow meters is not high, and the measurement error is large, which affects the normal industrial production process. This method is used to analyze the influence of the structural parameters of the flowmeter on its measurement accuracy, and the four most important structural parameters are found and optimized. The measurement error of the Bitoba flowmeter is reduced from 1% to 0.2%, and the measurement repeatability is reduced from 0.3 to 0.06, which proves the effectiveness of the method.

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Small area estimation of receiver operating characteristic curves for ordinal data under stochastic ordering

Cited by 6 publications

References 37 publications

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Clinicopathological Characteristics and Prognosis of Nasopharyngeal Lymphoepithelial Carcinoma: A Population-Based Retrospective Study

Application of Characteristic Model-Based Principal Component Analysis in Optimization of Flowmeter Parameters

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