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Journal articles on the topic 'AREA UNDER THE ROC-CURVE (AUC)'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Janssens, A. Cecile J. W., and Forike K. Martens. "Reflection on modern methods: Revisiting the area under the ROC Curve." International Journal of Epidemiology 49, no. 4 (2020): 1397–403. http://dx.doi.org/10.1093/ije/dyz274.

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Abstract The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk

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Marzban, Caren. "The ROC Curve and the Area under It as Performance Measures." Weather and Forecasting 19, no. 6 (2004): 1106–14. http://dx.doi.org/10.1175/825.1.

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Abstract The receiver operating characteristic (ROC) curve is a two-dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. In this short article, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features of the underlying distribution of forecasts. This allows for an interpretation of the former in terms of the latter. The analysis is pedagogical in that many of the findings are already known in more general (and more realistic) settings; however, the simplicit

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Narasimhan, Harikrishna, and Shivani Agarwal. "Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve." Neural Computation 29, no. 7 (2017): 1919–63. http://dx.doi.org/10.1162/neco_a_00972.

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The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vector algorithms for directly optimizing the partial AUC between any two false-positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the

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Cortese, Giuliana. "Estimating the Area under the ROC Curve with Modified Profile Likelihoods." International Journal of Statistics and Probability 6, no. 1 (2016): 1. http://dx.doi.org/10.5539/ijsp.v6n1p1.

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Receiver operating characteristic (ROC) curves are a frequent tool to study the discriminating ability of a certain characteristic. The area under the ROC curve (AUC) is a widely used measure of statistical accuracy of continuous markers for diagnostic tests, and has the advantage of providing a single summary index of overall performance of the test. Recent studies have shown some critical issues related to traditional point and interval estimates for the AUC, especially for small samples, more complex models, unbalanced samples or values near the boundary of the parameter space, i.e., when t

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Ekström, Joakim, Jim Åkerrén Ögren, and Tobias Sjöblom. "Exact Probability Distribution for the ROC Area under Curve." Cancers 15, no. 6 (2023): 1788. http://dx.doi.org/10.3390/cancers15061788.

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The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus the exactness in its probability distribution is crucial toward accurate statistical inference. We show the exact probability distribution of the ROC AUC-value, hence exact critical values and p-values are readily obtained. Because the exact calculations are computationally intense, we demonstrate a method of geometric interpolation, which is exact in a special case but generally an approximation, vastly increasing computational speeds. The meth

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Liu, Siyan, Qinglong Tian, Yukun Liu, and Pengfei Li. "Joint Statistical Inference for the Area under the ROC Curve and Youden Index under a Density Ratio Model." Mathematics 12, no. 13 (2024): 2118. http://dx.doi.org/10.3390/math12132118.

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The receiver operating characteristic (ROC) curve is a valuable statistical tool in medical research. It assesses a biomarker’s ability to distinguish between diseased and healthy individuals. The area under the ROC curve (AUC) and the Youden index (J) are common summary indices used to evaluate a biomarker’s diagnostic accuracy. Simultaneously examining AUC and J offers a more comprehensive understanding of the ROC curve’s characteristics. In this paper, we utilize a semiparametric density ratio model to link the distributions of a biomarker for healthy and diseased individuals. Under this mo

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Kochański, Błażej. "The shape of an ROC curve in the evaluation of credit scoring models." Statistics in Transition new series 25, no. 2 (2024): 205–18. http://dx.doi.org/10.59170/stattrans-2024-022.

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The AUC, i.e. the area under the receiver operating characteristic (ROC) curve, or its scaled version, the Gini coefficient, are the standard measures of the discriminatory power of credit scoring. Using binormal ROC curve models, we show how the shape of the curves affects the economic benefits of using scoring models with the same AUC. Based on the results, we propose that the shape parameter of the fitted ROC curve is reported alongside its AUC/Gini whenever the quality of a scorecard is discussed.

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Tang, Ke, Rui Wang, and Tianshi Chen. "Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 483–88. http://dx.doi.org/10.1609/aaai.v25i1.7901.

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The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution and misclassification costs may be unknown during training process or even change in environment. MAUC, the extension of AUC to multi-class problems, has also attracted a lot of attention. However, despite the emergence of approaches for training classifiers with large AUC, little has been done for

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Vardhan, R. Vishnu, and S. Balaswamy. "Improved Methods for Estimating Areas under the Receiver Operating Characteristic Curves." International Journal of Green Computing 4, no. 2 (2013): 58–75. http://dx.doi.org/10.4018/jgc.2013070105.

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ROC Curve is the most widely used statistical technique for classifying an individual into one of the two pre-determined groups basing on test result. Area under the curve (AUC) is a measure of accuracy which exhibits the discriminating power of the test with respect to a threshold or cutoff value. In medical diagnosis, this technique has its relevance to study and compare different diagnostic tests. In this paper, a method is proposed to estimate the AUC of Binormal ROC model by taking into account the confidence interval of mean and corresponding variances.

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Takenouchi, Takashi, Osamu Komori, and Shinto Eguchi. "An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification." Neural Computation 24, no. 10 (2012): 2789–824. http://dx.doi.org/10.1162/neco_a_00336.

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While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimu

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Bai, Kevin Z., and John M. Fossaceca. "EM-AUC: A Novel Algorithm for Evaluating Anomaly Based Network Intrusion Detection Systems." Sensors 25, no. 1 (2024): 78. https://doi.org/10.3390/s25010078.

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Effective network intrusion detection using anomaly scores from unsupervised machine learning models depends on the performance of the models. Although unsupervised models do not require labels during the training and testing phases, the assessment of their performance metrics during the evaluation phase still requires comparing anomaly scores against labels. In real-world scenarios, the absence of labels in massive network datasets makes it infeasible to calculate performance metrics. Therefore, it is valuable to develop an algorithm that calculates robust performance metrics without using la

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Filipa Mourão, Maria, Ana Cristina Braga, and Pedro Nuno Oliveira. "CRIB conditional on gender: nonparametric ROC curve." International Journal of Health Care Quality Assurance 27, no. 8 (2014): 656–63. http://dx.doi.org/10.1108/ijhcqa-04-2013-0047.

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Purpose – The purpose of this paper is to use the kernel method to produce a smoothed receiver operating characteristic (ROC) curve and show how baby gender can influence Clinical Risk Index for Babies (CRIB) scale according to survival risks. Design/methodology/approach – To obtain the ROC curve, conditioned by covariates, two methods may be followed: first, indirect adjustment, in which the covariate is first modeled within groups and then by generating a modified distribution curve; second, direct smoothing in which covariate effects is modeled within the ROC curve itself. To verify if new-

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Berrar, D. "An Empirical Evaluation of Ranking Measures With Respect to Robustness to Noise." Journal of Artificial Intelligence Research 49 (February 17, 2014): 241–67. http://dx.doi.org/10.1613/jair.4136.

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Ranking measures play an important role in model evaluation and selection. Using both synthetic and real-world data sets, we investigate how different types and levels of noise affect the area under the ROC curve (AUC), the area under the ROC convex hull, the scored AUC, the Kolmogorov-Smirnov statistic, and the H-measure. In our experiments, the AUC was, overall, the most robust among these measures, thereby reinvigorating it as a reliable metric despite its well-known deficiencies. This paper also introduces a novel ranking measure, which is remarkably robust to noise yet conceptually simple

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Luque-Fernandez, Miguel Angel, Daniel Redondo-Sánchez, and Camille Maringe. "cvauroc: Command to compute cross-validated area under the curve for ROC analysis after predictive modeling for binary outcomes." Stata Journal: Promoting communications on statistics and Stata 19, no. 3 (2019): 615–25. http://dx.doi.org/10.1177/1536867x19874237.

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Receiver operating characteristic (ROC) analysis is used for comparing predictive models in both model selection and model evaluation. ROC analysis is often applied in clinical medicine and social science to assess the tradeoff between model sensitivity and specificity. After fitting a binary logistic or probit regression model with a set of independent variables, the predictive performance of this set of variables can be assessed by the area under the curve (AUC) from an ROC curve. An important aspect of predictive modeling (regardless of model type) is the ability of a model to generalize to

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Andi Sitti Nur, Afiah, Bahrun Uleng, Massi Muhammad Nasrum, and Djaharuddin Irawaty. "Expression of mIRNA-142-3P in Peripheral Blood From Active and Latent Pulmonary Tuberculosis." Journal of Neonatal Surgery 14, no. 1S (2025): 649–57. https://doi.org/10.52783/jns.v14.1588.

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This study aimed to asses miRNA-142-3p expression of active and latent pulmonary tuberculosis. 33 people with active Tuberculosis (TB), 36 household contacts who tested positive for IFN-released assay (IGRA), and 39 healthy controls made up this case-control study. Latent pulmonary tuberculosis infection in household contacts was identified using an IGRA based on an enzyme-linked immunosorbent test. miRNA-142-3p expression was measured by quantitative real-time PCR. Data analysis used analysed of variance and receiver operating characteristic (ROC) curves. The results showed that miRNA-142-3p

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KURTCEPHE, MURAT, and H. ALTAY GÜVENIR. "A DISCRETIZATION METHOD BASED ON MAXIMIZING THE AREA UNDER RECEIVER OPERATING CHARACTERISTIC CURVE." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 01 (2013): 1350002. http://dx.doi.org/10.1142/s021800141350002x.

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Many machine learning algorithms require the features to be categorical. Hence, they require all numeric-valued data to be discretized into intervals. In this paper, we present a new discretization method based on the receiver operating characteristics (ROC) Curve (AUC) measure. Maximum area under ROC curve-based discretization (MAD) is a global, static and supervised discretization method. MAD uses the sorted order of the continuous values of a feature and discretizes the feature in such a way that the AUC based on that feature is to be maximized. The proposed method is compared with alternat

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Khodadadi, Babak, Nazanin Mousavi, Mahshad Mousavi, Parastoo Baharvand, and Seyyed Amir Yasin Ahmadi. "Diagnosis and predictive clinical and para-clinical cutoffs for diabetes complications in Lur and Lak populations of Iran; a ROC curve analysis to design a regional guideline." Journal of Nephropharmacology 7, no. 2 (2018): 83–89. http://dx.doi.org/10.15171/npj.2018.19.

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Introduction: American Diabetes Association updates its guideline every year. However this guideline can be changed for different populations based on their cultural and genetic status. Objectives: We intend to design a regional study in Lur and Lak populations of Iran using receiver operating characteristics (ROC) curve model. Patients and Methods: A total of 133 diabetes mellitus (DM) patients were enrolled in this study. The collected information for each patient were gender, age, body mass index (BMI), DM type, DM duration, fasting blood sugar (FBS), hemoglobin A1c (HbA1c), lipid profile,

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Zhou, Xiang, Wenyu Zhang, and Yefeng Jiang. "Personal Credit Default Prediction Model Based on Convolution Neural Network." Mathematical Problems in Engineering 2020 (October 5, 2020): 1–10. http://dx.doi.org/10.1155/2020/5608392.

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It has great significance for the healthy development of credit industry to control the credit default risk by using the information technology. For some traditional research about the credit default prediction model, more attention is paid to the model accuracy, while the business characteristics of the credit risk prevention are easy to be ignored. Meanwhile, to reduce the complicity of the model, the data features need be extracted manually, which will decrease the high-dimensional correlation among the analyzing data and then result in the low prediction performance of the model. So, in th

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Nahm, Francis Sahngun. "Receiver operating characteristic curve: overview and practical use for clinicians." Korean Journal of Anesthesiology 75, no. 1 (2022): 25–36. http://dx.doi.org/10.4097/kja.21209.

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Using diagnostic testing to determine the presence or absence of a disease is essential in clinical practice. In many cases, test results are obtained as continuous values and require a process of conversion and interpretation and into a dichotomous form to determine the presence of a disease. The primary method used for this process is the receiver operating characteristic (ROC) curve. The ROC curve is used to assess the overall diagnostic performance of a test and to compare the performance of two or more diagnostic tests. It is also used to select an optimal cut-off value for determining th

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Li, Jing. "Area under the ROC Curve has the most consistent evaluation for binary classification." PLOS ONE 19, no. 12 (2024): e0316019. https://doi.org/10.1371/journal.pone.0316019.

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The proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence while the relationships between different variables and the sample size are kept constant. Analyzing 156 data scenarios, 18 model evaluation metrics and five commonly used machine learning models as well as a naive random guess model, I find that evaluation metrics that are less influenced by prevalence offer more consistent evaluation of individual m

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Khan, Javaria Ahmad, and Atif Akbar. "Logistic Kernel: A Sensitive Biomarker for Kidney Cancer by ROC Curve." International Journal of Applied Sciences & Development 2 (October 16, 2023): 120–32. http://dx.doi.org/10.37394/232029.2023.2.13.

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The receiver operating characteristic (ROC) curve is a well-known graphical method to describe the accuracy of a diagnostic test. In this paper, Logistic kernel is proposed with its optimal bandwidth and mean squared error. To observe the performance of our proposed kernel estimator, the comparison is made with a Gaussian kernel by using different bandwidths and ROC curve and the area under the curve (AUC) are calculated. For illustration, Kidney cancer data is used and the logistic kernel is found more pragmatic and sensitive biomarker to detect Kidney cancer. The outstanding performance of l

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Lambert, Jerome, Philippe Halfon, Guillaume Penaranda, Pierre Bedossa, Patrice Cacoub, and Fabrice Carrat. "How to Measure the Diagnostic Accuracy of Noninvasive Liver Fibrosis Indices: The Area Under the ROC Curve Revisited." Clinical Chemistry 54, no. 8 (2008): 1372–78. http://dx.doi.org/10.1373/clinchem.2007.097923.

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Abstract Background: The area under the ROC curve (AUC) is widely used to measure the diagnostic accuracy of noninvasive fibrosis indices. However, use of the AUC assumes a binary gold standard, whereas fibrosis staging is based on an ordinal scale and also depends on the distribution of fibrosis stages in the study sample. We explored other fibrosis staging accuracy measures designed for ordinal gold standards, the C-statistic and the Obuchowski measure. Methods: We performed a simulation study to assess the bias in estimating the accuracy measures when the distribution of fibrosis stages in

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Shaikh, Yasmeen, Vasudev Parvati, and Sangappa Ramachandra Biradar. "Early disease prediction algorithm for hypertension-based diseases using data aware algorithms." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1100–1108. https://doi.org/10.11591/ijeecs.v27.i2.pp1100-1108.

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This paper implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanc

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Feng, Dai, Giuliana Cortese, and Richard Baumgartner. "A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size." Statistical Methods in Medical Research 26, no. 6 (2015): 2603–21. http://dx.doi.org/10.1177/0962280215602040.

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The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the constructi

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Yang, Hanfang, Kun Lu, Xiang Lyu, and Feifang Hu. "Two-way partial AUC and its properties." Statistical Methods in Medical Research 28, no. 1 (2017): 184–95. http://dx.doi.org/10.1177/0962280217718866.

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Simultaneous control on true positive rate and false positive rate is of significant importance in the performance evaluation of diagnostic tests. Most of the established literature utilizes partial area under receiver operating characteristic (ROC) curve with restrictions only on false positive rate (FPR), called FPR pAUC, as a performance measure. However, its indirect control on true positive rate (TPR) is conceptually and practically misleading. In this paper, a novel and intuitive performance measure, named as two-way pAUC, is proposed, which directly quantifies partial area under ROC cur

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Ramesh, Jayanthy, Johann Varghese, S. L. Sagar Reddy, and Moganti Rajesh. "Systemic inflammatory index a simple marker of thrombo-inflammation and prognosis in severe COVID-19 patients." International Journal of Advances in Medicine 8, no. 9 (2021): 1335. http://dx.doi.org/10.18203/2349-3933.ijam20213165.

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Background: COVID-19 pandemic has challenged the healthcare resources globally, inspiring the need for identifying simple, economical biomarkers. COVID-19 is an immune-inflammatory disorder and systemic inflammatory index (SII) derived from the peripheral blood has been proposed as a marker.Methods: Retrospective study of severe COVID-19 hospitalized patients (total N=154 including diabetic subset N=57). Data regarding hematological variables such as absolute neutrophil count (ANC), absolute lymphocyte count (ALC), platelet count along with thrombo-inflammatory proteins, D-dimer, C-reactive pr

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Shaikh, Yasmeen, Vasudev Parvati, and Sangappa Ramachandra Biradar. "Early disease prediction algorithm for hypertension-based diseases using data aware algorithms." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1100. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1100-1108.

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This paper <span lang="EN-US">implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to ei

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Chakrabartty, Satyendra Nath. "Screening and Diagnostic tests for Health-related Quality of Life." New Healthcare Advancements and Explorations 01, no. 02 (2024): 01–08. https://doi.org/10.64347/3066-2591/nhae.006.

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Objective: The paper describes conceptual framework and methodological issues to find cut-off points considering summative scores with emphasis on Receiver Operating Characteristic (ROC) curve and associated components like area under the curve (AUC), sensitivity and specificity along with scoring and their properties. Material and Methods: To establish presence (or absence) of disease as a basis for treatment decisions, diagnostic tests require meaningful total/domain scores. Problems of evaluation of Health-related Quality of Life (HRQoL) scales and remedial measures by transforming item sco

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Radzevičienė, Aurelija, Pierre Marquet, Rima Maslauskienė, Rūta Vaičiūnienė, Edmundas Kaduševičius, and Edgaras Stankevičius. "Analyses of AUC(0–12) and C0 Compliances within Therapeutic Ranges in Kidney Recipients Receiving Cyclosporine or Tacrolimus." Journal of Clinical Medicine 9, no. 12 (2020): 3903. http://dx.doi.org/10.3390/jcm9123903.

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The AUC (area under the concentration time curve) is considered the pharmacokinetic exposure parameter best associated with clinical effects. Unfortunately, no prospective studies of clinical outcomes have been conducted in adult transplant recipients to investigate properly the potential benefits of AUC(0–12) monitoring compared to the C0-guided therapy. The aim of the present study was to compare two methods, C0 (through level) and AUC(0–12) (area under the concentration time curve), for assessing cyclosporine and tacrolimus concentrations. The study included 340 kidney recipients. The AUC(0

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Wang, Bin, Shengliang Wang, Shaolei Lang, et al. "Image Risk Assessment of the Thyroid Cancer Model Based on Discriminant Analysis and the Value of TAP and CEA Combined Detection." Journal of Healthcare Engineering 2021 (August 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/8836288.

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The incidence rate of thyroid disease is increasing rapidly worldwide, and the number of thyroid patients is increasing. In this study, serum TAP (tumor abnormal protein) and CEA (carcinoembryonic antigen) were used to detect patients with thyroid nodules of class IV and above to explore the value of serum TAP combined detection of CEA in the risk assessment of thyroid cancer. In this paper, 400 patients with thyroid nodules above class IV diagnosed by physical examination in our hospital health management center from January 2019 to June 2021 were included in the study. Combined with the path

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Wang, Qingjun, Yong Guo, Jing Zhang, Zijun Wang, Minhua Huang, and Yun Zhang. "Contribution of IVIM to Conventional Dynamic Contrast-Enhanced and Diffusion-Weighted MRI in Differentiating Benign from Malignant Breast Masses." Breast Care 11, no. 4 (2016): 254–58. http://dx.doi.org/10.1159/000447765.

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Background: The aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterization of benign and malignant breast masses compared with conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI). Patients and Methods: This study included 23 benign and 31 malignant breast masses of 48 patients. Main indicators were initial enhancement ratio (IER), time-signal intensity curve (TIC), apparent diffusion coefficient (ADC), tissue diffusivity (D

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Brzezinski, Dariusz, and Jerzy Stefanowski. "Prequential AUC: properties of the area under the ROC curve for data streams with concept drift." Knowledge and Information Systems 52, no. 2 (2017): 531–62. http://dx.doi.org/10.1007/s10115-017-1022-8.

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Park, Hoon Suk, Chan Joon Kim, Jeong-Eun Yi, et al. "Contrast Volume/Raw eGFR Ratio for Predicting Contrast-Induced Acute Kidney Injury in Patients Undergoing Percutaneous Coronary Intervention for Myocardial Infarction." Cardiorenal Medicine 5, no. 1 (2015): 61–68. http://dx.doi.org/10.1159/000369940.

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Background: Considering that contrast medium is excreted through the whole kidney in a similar manner to drug excretion, the use of raw estimated glomerular filtration rate (eGFR) rather than body surface area (BSA)-normalized eGFR is thought to be more appropriate for evaluating the risk of contrast-induced acute kidney injury (CI-AKI). Methods: This study included 2,189 myocardial infarction patients treated with percutaneous coronary intervention. Logistic regression analysis was performed to identify the independent risk factors. We used receiver-operating characteristic (ROC) curves to co

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Khoshtinat, Saeed, Babak Aminnejad, Yousef Hassanzadeh, and Hasan Ahmadi. "Application of GIS-based models of weights of evidence, weighting factor, and statistical index in spatial modeling of groundwater." Journal of Hydroinformatics 21, no. 5 (2019): 745–60. http://dx.doi.org/10.2166/hydro.2019.127.

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Abstract The present research aims at applying three geographic information system (GIS)-based bivariate models, namely, weights of evidence (WOE), weighting factor (WF), and statistical index (SI), for mapping of groundwater potential for sustainable groundwater management. The locations of wells with groundwater yields more than 11 m3/h were selected for modeling. Then, these locations were grouped into two categories with 70% (52 locations) in a training dataset to build the model and 30% (22 locations) in a testing dataset to validate it. Conditioning factors, namely, altitude, slope degre

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Rubiyanti, Tien, Ida Widaningrum, and Andy Triyanto. "ANALISA KECELAKAAN LALU LINTAS MENGGUNAKAN METODE ALGORITMA C4.5 DAN NAÏVE BAYES (STUDI KASUS DI KABUPATEN PONOROGO)." KOMPUTEK 2, no. 1 (2018): 69. http://dx.doi.org/10.24269/jkt.v2i1.69.

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Penelitian ini menerapkan metode data mining yang kemudian dilakukan komparasi dari dua metode yang berbeda yaitu Algoritma C4.5 dan Naïve Bayes yang dikaji untuk memperoleh nilai akurasi yang terbaik berdasarkan data kecelakaan lalu lintas yang ada di Kota Ponorogo untuk mengetahui penyebab kecelakaan lalu lintas dengan kategori faktor pengemudi, faktor jalan, faktor cuaca dan faktor kendaraan. Kedua algoritma tersebut dibantu dengan perangkat lunak Weka yang berbasis Open Source (GPL) dan berengine Java. Maka hasil pengujian kedua algoritma tersebut diuji menggunakan Confusion Matrix dan Ku

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Khan, Arfat Ahmad, Muhammad Asif Nauman, Muhammad Shoaib, et al. "Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model." Electronics 11, no. 19 (2022): 3105. http://dx.doi.org/10.3390/electronics11193105.

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This study proposed an AlexNet-based crowd anomaly detection model in the video (image frames). The proposed model was comprised of four convolution layers (CLs) and three Fully Connected layers (FC). The Rectified Linear Unit (ReLU) was used as an activation function, and weights were adjusted through the backpropagation process. The first two CLs are followed by max-pool layer and batch normalization. The CLs produced features that are utilized to detect the anomaly in the image frame. The proposed model was evaluated using two parameters—Area Under the Curve (AUC) using Receiver Operator Ch

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Foley, Christina S., Edwina C. Moore, Mira Milas, Eren Berber, Joyce Shin, and Allan E. Siperstein. "RECEIVER OPERATING CHARACTERISTIC ANALYSIS OF INTRAOPERATIVE PARATHYROID HORMONE MONITORING TO DETERMINE OPTIMUM SENSITIVITY AND SPECIFICITY: ANALYSIS OF 896 CASES." Endocrine Practice 25, no. 11 (2019): 1117–26. http://dx.doi.org/10.4158/ep-2019-0191.

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Objective: While intraoperative parathyroid hormone (IOPTH) monitoring with a ≥50% drop commonly guides the extent of exploration for primary hyperparathyroidism (pHPT), receiver operating characteristic (ROC) analysis has not been performed to determine whether other criteria yield better sensitivity and specificity. The aim of this study was to identify the optimum percent change of IOPTH following removal of the abnormal parathyroid pathology, in order to predict biochemical cure. Secondary aims were to identify patient subgroups with increased area under the ROC curve (AUC) and the need fo

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Chen, Li-Pang. "Analysis of Receiver Operating Characteristic Curves for Cure Survival Data and Mismeasured Biomarkers." Mathematics 13, no. 3 (2025): 424. https://doi.org/10.3390/math13030424.

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Cure models and receiver operating characteristic (ROC) curve estimation are two important issues in survival analysis and have received attention for many years. In the development of biostatistics, these two topics have been well discussed separately. However, a rare development in the estimation of the ROC curve has been made available based on survival data with the cure fraction. On the other hand, while a large body of estimation methods have been proposed, they rely on an implicit assumption that the variables are precisely measured. In applications, measurement errors are generally ubi

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Kim, Hyuntae, Ji-Soo Song, Teo Jeon Shin, et al. "Detection of Proximal Caries Lesions with Deep Learning Algorithm." JOURNAL OF THE KOREAN ACADEMY OF PEDTATRIC DENTISTRY 49, no. 2 (2022): 131–39. http://dx.doi.org/10.5933/jkapd.2022.49.2.131.

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This study aimed to evaluate the effectiveness of deep convolutional neural networks (CNNs) for diagnosis of interproximal caries in pediatric intraoral radiographs. A total of 500 intraoral radiographic images of first and second primary molars were used for the study. A CNN model (Resnet 50) was applied for the detection of proximal caries. The diagnostic accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) were calculated on the test dataset. The diagnostic accuracy was 0.84, sensitivity was 0.74, and specificity was 0.94. The tra

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Lei, Jingchao, Jia Zhai, Yao Zhang, Jing Qi, and Chuanzheng Sun. "Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study." Journal of Medical Internet Research 27 (May 26, 2025): e66733. https://doi.org/10.2196/66733.

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Background Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early identification of SALI can improve patient outcomes; however, sepsis heterogeneity makes timely diagnosis challenging. Traditional diagnostic tools are often limited, and machine learning techniques offer promising solutions for predicting adverse outcomes in patients with sepsis. Objective This study aims to develop an explainable machine learning model, incorporating stacking techniques, to predict the occurrence of liver injury in patients with sep

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Lv, Tiao, Yinghong Zhang, Wen Zhang, et al. "Application Value of Rapid Predictive Model for Readmission Risk in Patients after CABG." Heart Surgery Forum 23, no. 5 (2020): E668—E672. http://dx.doi.org/10.1532/hsf.3133.

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Objective: To explore the value of a rapid risk predictive model for the readmission of patients after CABG in China. Methods: The rapid predictive model of readmission risk was translated into Chinese, and then validated with data from 758 patients who underwent CABG in Wuhan Asian Heart Hospital from January 2018 to June 2019. The discrimination was tested by area under the ROC curve (AUC), and the calibration was tested by Hosmer-Lemeshow test. Results: The rapid risk predictive model for readmission showed good discrimination and calibration in Chinese CABG patients (The area under ROC cur

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Ivanova, Tatyana, Nataliya Sychenkova, Vera Khorokhorina, Nikolay Ryabchenko, Sergey Ivanov, and Lyudmila Krikunova. "THE ASSOCIATION OF THE APOLIPOPROTEIN E4 ALLELE (RS 429358) WITH OVARIAN SEROUS ADENOCARCINOMA." Problems in oncology 63, no. 4 (2017): 627–31. http://dx.doi.org/10.37469/0507-3758-2017-63-4-627-631.

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The distribution of APOE 4 allele (rs 429358, C) was analyzed in healthy women (N=454) and patients with ovarian serous adenocarcinoma (N=114) in order to identify genetic predisposition to the disease. We determined the prognostic indicators of the E4 allele as a marker: odds ratio (OR) and AUC (Area Under Curve) - an area under the ROC curve. It was shown that APOE 4 allele was significantly associated with ovarian serous adenocarcinoma (p = 0,003; 0R=1,94; AUC=0,55). The Е4 genotypes frequency was significantly increased among patients (p = 0.02; 0R=1,8). Separate analysis of the two age su

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Wang, Xiaoxia, Xinrong Wang, Tingfan Wu, et al. "Computed tomography-based radiomics to assess risk stratification in pediatric malignant peripheral neuroblastic tumors." Medicine 102, no. 47 (2023): e35690. http://dx.doi.org/10.1097/md.0000000000035690.

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This study aimed to develop and validate an analysis system based on preoperative computed tomography (CT) to predict the risk stratification in pediatric malignant peripheral neuroblastic tumors (PNTs). A total of 405 patients with malignant PNTs (184 girls and 221 boys; mean age, 33.8 ± 29.1 months) were retrospectively evaluated between January 2010 and June 2018. Radiomic features were extracted from manually segmented tumors on preoperative CT images. Spearman’s rank correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were used to eliminate redundancy a

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Younis, Fazla, and Omar Zafar. "Comparison of Scheimpflug Derived Biomechanical and Tomographical Indices to Detect Corneal Ectasia - A Cross Sectional Study." Pakistan Armed Forces Medical Journal 72, SUPPL-2 (2022): S122–26. http://dx.doi.org/10.51253/pafmj.v72isuppl-2.3284.

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Objective: To compare Corvis ST (Oculus Wetzlar, Germany) and Pentacam (Oculus) derived indices to detect normal cornea, keratoconus and for mefruste keratoconus. Study Design: Comparative cross sectional study. Place and Duration of Study: Armed Forces Institute of Ophthalmology (AFIO), Rawalpindi, from Feb to Jul 2019. Methodology: Following strict inclusion criteria 90 patients were enrolled that were divided into 3 equal groups of normal, Keratoconus and Formefruste kertoconus. One eye of the patient was selected and underwent ophthalmic examination followed by assessment

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Sakai, Kotomi, Stuart Gilmour, Eri Hoshino, et al. "A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition." Nutrients 13, no. 11 (2021): 4009. http://dx.doi.org/10.3390/nu13114009.

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Background: Sarcopenic dysphagia, a swallowing disorder caused by sarcopenia, is prevalent in older patients and can cause malnutrition and aspiration pneumonia. This study aimed to develop a simple screening test using image recognition with a low risk of droplet transmission for sarcopenic dysphagia. Methods: Older patients admitted to a post-acute care hospital were enrolled in this cross-sectional study. As a main variable for the development of a screening test, we photographed the anterior neck to analyze the image features of sarcopenic dysphagia. The studied image features included the

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Yang, Jingyan, Christine L. Sardo Molmenti, Joaquin Cagliani, et al. "Time-Effect of Donor and Recipient Characteristics on Graft Survival after Kidney Transplantation." International Journal of Angiology 28, no. 04 (2019): 249–54. http://dx.doi.org/10.1055/s-0039-1700500.

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AbstractThe kidney allocation system (KAS) is based on quality-based “longevity matching” strategies that provide only a momentary snapshot of expected outcomes at the time of transplantation. The purpose of our study was to define on a continuous timeline the relative and mutual interactions of donor and recipient characteristics on graft survival.Total 39,108 subjects who underwent kidney transplant between October 25, 1999 and January 1, 2007 were identified in the United Network for Organ Sharing dataset. Our primary outcome was graft survival. Time-dependent receiver operating characteris

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Cho, Ki-Ho, Kyoo-Pil Kim, Byung-Cheol Woo, et al. "Relationship between Blood Stasis Syndrome Score and Cardioankle Vascular Index in Stroke Patients." Evidence-Based Complementary and Alternative Medicine 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/696983.

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Blood stasis syndrome (BSS) in traditional Asian medicine has been considered to correlate with the extent of atherosclerosis, which can be estimated using the cardioankle vascular index (CAVI). Here, the diagnostic utility of CAVI in predicting BSS was examined. The BSS scores and CAVI were measured in 140 stroke patients and evaluated with respect to stroke risk factors. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic accuracy of CAVI for the diagnosis of BSS. The BSS scores correlated significantly with CAVI, age, and systolic blood pressure (SBP)

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Bhattarai, Binod, and Shashi Bhushan Sah. "Clinical characteristics and radiological domains among patients with recurrent strokes-a descriptive cross-sectional study from a tertiary care center in central Nepal." F1000Research 10 (August 5, 2021): 757. http://dx.doi.org/10.12688/f1000research.54981.1.

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Background: Stroke is a significant global health hazard that ripples continuum multi-spectral effects to the patients as well their caretakers. Methods: We studied 28 consecutive cohorts of patients with recurrent strokes managed in our centre within the last two years. Results: The most common recurrence stroke pattern was of that of hemorrhagic to hemorrhagic subtype observed in 50% of the patients. The most common anatomical region of involvement was cortical – cortical seen in 39.28% of our cohorts. The surgical intervention was required in 17.85% whereas 42.85% of them were managed conse

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Wei, Chih Chiang. "Receiver Operating Characteristic for Diagnosis of Wine Quality by Bayesian Network Classifiers." Advanced Materials Research 591-593 (November 2012): 1168–73. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1168.

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This paper is dedicated to demonstrate the use of the receiver operating characteristic (ROC) and the area under the ROC curve (AUC) for diagnosing forecast skill. Several local search heuristic algorithms to discover which one performs better for learning a certain Bayesian networks (BN). Five heuristic search algorithms, including K2, Hill Climbing, Repeated Hill Climber, LAGD Hill Climbing, and TAN, were empirically evaluated and compared. This study tests BN models in a real-world case, the Vinho Verde wine taste preferences. An average AUC of 0.746 and 0.727 respectively in red wine and w

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Niroomand, Maximilian P., Conor T. Cafolla, John W. R. Morgan, and David J. Wales. "Characterising the area under the curve loss function landscape." Machine Learning: Science and Technology 3, no. 1 (2022): 015019. http://dx.doi.org/10.1088/2632-2153/ac49a9.

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Abstract One of the most common metrics to evaluate neural network classifiers is the area under the receiver operating characteristic curve (AUC). However, optimisation of the AUC as the loss function during network training is not a standard procedure. Here we compare minimising the cross-entropy (CE) loss and optimising the AUC directly. In particular, we analyse the loss function landscape (LFL) of approximate AUC (appAUC) loss functions to discover the organisation of this solution space. We discuss various surrogates for AUC approximation and show their differences. We find that the char

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