Relevant bibliographies by topics / Receiver Operating Characteristic (ROC) Curve

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Receiver Operating Characteristic (ROC) Curve'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Receiver Operating Characteristic (ROC) Curve"

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Yang, Shengping, and Gilbert Berdine. "The receiver operating characteristic (ROC) curve." Southwest Respiratory and Critical Care Chronicles 5, no. 19 (May 5, 2017): 34. http://dx.doi.org/10.12746/swrccc.v5i19.391.

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Fan, Jerome, Suneel Upadhye, and Andrew Worster. "Understanding receiver operating characteristic (ROC) curves." CJEM 8, no. 01 (January 2006): 19–20. http://dx.doi.org/10.1017/s1481803500013336.

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In this issue of the Journal, Auer and colleagues conclude that serum levels of neuron-specific enolase (NSE), a biochemical marker of ischemic brain injury, may have clinical utility for the prediction of survival to hospital discharge in patients experiencing the return of spontaneous circulation following at least 5 minutes of cardiopulmonary resuscitation. The authors used a receiver operating characteristic (ROC) curve to illustrate and evaluate the diagnostic (prognostic) performance of NSE. We explain ROC curve analysis in the following paragraphs.
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Nahm, Francis Sahngun. "Receiver operating characteristic curve: overview and practical use for clinicians." Korean Journal of Anesthesiology 75, no. 1 (February 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 the presence or absence of a disease. Although clinicians who do not have expertise in statistics do not need to understand both the complex mathematical equation and the analytic process of ROC curves, understanding the core concepts of the ROC curve analysis is a prerequisite for the proper use and interpretation of the ROC curve. This review describes the basic concepts for the correct use and interpretation of the ROC curve, including parametric/nonparametric ROC curves, the meaning of the area under the ROC curve (AUC), the partial AUC, methods for selecting the best cut-off value, and the statistical software to use for ROC curve analyses.
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Kumar, Rajeev, and Abhaya Indrayan. "Receiver operating characteristic (ROC) curve for medical researchers." Indian Pediatrics 48, no. 4 (April 2011): 277–87. http://dx.doi.org/10.1007/s13312-011-0055-4.

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Park, Seong Ho, Jin Mo Goo, and Chan-Hee Jo. "Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists." Korean Journal of Radiology 5, no. 1 (2004): 11. http://dx.doi.org/10.3348/kjr.2004.5.1.11.

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Kawada, Tomoyuki. "Sample Size in Receiver-Operating Characteristic (ROC) Curve Analysis." Circulation Journal 76, no. 3 (2012): 768. http://dx.doi.org/10.1253/circj.cj-11-1408.

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Yoshinaga, Keiichiro, Kazumasa Tsukamoto, and Nagara Tamaki. "Sample Size in Receiver-Operating Characteristic (ROC) Curve Analysis." Circulation Journal 76, no. 3 (2012): 769. http://dx.doi.org/10.1253/circj.cj-11-1503.

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Cook, Jonathan A., and Ashish Rajbhandari. "Heckroccurve: ROC Curves for Selected Samples." Stata Journal: Promoting communications on statistics and Stata 18, no. 1 (March 2018): 174–83. http://dx.doi.org/10.1177/1536867x1801800110.

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Receiver operating characteristic (ROC) curves can be misleading when they are constructed with selected samples. In this article, we describe heckroccurve, which implements a recently developed procedure for plotting ROC curves with selected samples. The command estimates the area under the ROC curve and a graphical display of the curve. A variety of plot options are available, including the ability to add confidence bands to the plot.
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Hays, Ron D. "ROC: Estimation of the Area Under a Receiver Operating Characteristic Curve." Applied Psychological Measurement 14, no. 2 (June 1990): 208. http://dx.doi.org/10.1177/014662169001400209.

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Morrison, Ann Michelle, Kelly Coughlin, James P. Shine, Brent A. Coull, and Andrea C. Rex. "Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables." Applied and Environmental Microbiology 69, no. 11 (November 2003): 6405–11. http://dx.doi.org/10.1128/aem.69.11.6405-6411.2003.

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ABSTRACT Receiver operating characteristic (ROC) curve analysis is a simple and effective means to compare the accuracies of indicator variables of bacterial beach water quality. The indicator variables examined in this study were previous day's Enterococcus density and antecedent rainfall at 24, 48, and 96 h. Daily Enterococcus densities and 15-min rainfall values were collected during a 5-year (1996 to 2000) study of four Boston Harbor beaches. The indicator variables were assessed for their ability to correctly classify water as suitable or unsuitable for swimming at a maximum threshold Enterococcus density of 104 CFU/100 ml. Sensitivity and specificity values were determined for each unique previous day's Enterococcus density and antecedent rainfall volume and used to construct ROC curves. The area under the ROC curve was used to compare the accuracies of the indicator variables. Twenty-four-hour antecedent rainfall classified elevated Enterococcus densities more accurately than previous day's Enterococcus density (P = 0.079). An empirically derived threshold for 48-h antecedent rainfall, corresponding to a sensitivity of 0.75, was determined from the 1996 to 2000 data and evaluated to ascertain if the threshold would produce a 0.75 sensitivity with independent water quality data collected in 2001 from the same beaches.
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Dissertations / Theses on the topic "Receiver Operating Characteristic (ROC) Curve"

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Caswell, Benjamin C. "Receiver Operating Characteristic (ROC) Curve Analysis of Affinity Profiles." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3012.pdf.

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Zhang, Zheng. "Semiparametric least squares analysis of the receiver operating characteristic curve /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9578.

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Dodd, Lori Elizabeth. "Regression methods for areas and partial areas under the receiver-operating characteristic curve /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9567.

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BORGSTROM, MARK CRAIG. "ESTIMATION OF RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE PARAMETERS: SMALL SAMPLE PROPERTIES OF ESTIMATORS." Diss., The University of Arizona, 1987. http://hdl.handle.net/10150/184127.

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When studying detection systems, parameters associated with the Receiver Operating Characteristic (ROC) curve are often estimated to assess system performance. In some applied settings it is often not possible to test the detection system with large numbers of stimuli. The resulting small sample statistics many have undesirable properties. The characteristics of these small sample ROC estimators were examined in a Monte Carlo simulation. Three popular ROC parameters were chosen for study. One of the parameters was a single parameter index of system performance, Area under the ROC curve. The other parameters, ROC intercept and slope, were considered as a pair. ROC intercept and slope were varied along with sample size and points on the certainty rating scale to form a four way factorial design. Several types of estimators were examined. For the parameter, Area under the curve, Maximum Likelihood (ML), three types of Least Squares (LS), and Distribution Free (DF) estimators were considered. Except for the DF estimator, the same estimators were considered for the parameters, intercept and slope. These estimators were compared with respect to three characteristics: bias, efficiency, and consistency. For Area under the curve, the ML estimator was the least biased. The DF estimator was the most efficient, and all the estimators except the DF estimator appeared to be consistent. For intercept and slope the LS estimator that minimized vertical error of the points from the ROC curve (line) was the least biased for both estimators. This LS estimator was also the most efficient. This estimator along with the ML estimator also appeared to be the most consistent. The other two estimators had no significant trend toward consistency. These results along with other findings, illustrate that different estimators may be "best" for different sample sizes and for different parameters. Therefore, researchers should carefully consider the characteristics of ROC estimators before using them as indices of system performance.
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Peng, Hongying. "ROC Curves for Ordinal Biomarkers." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543582492604243.

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池田, 充., Takeo Ishigaki, Mitsuru Ikeda, 一信 山内, and Kazunobu Yamauchi. "Relationship between Brier score and area under the binormal ROC curve." Elsevier, 2002. http://hdl.handle.net/2237/5310.

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Liu, Hua. "ASYMPTOTIC PROPERTIES OF PARTIAL AREAS UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE WITH APPLICATIONS IN MICROARRAY EXPERIMENTS." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_diss/463.

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Receiver operating characteristic (ROC) curves are widely used in medical decision making. It was recognized in the last decade that only a specific region of the ROC curve is of clinical interest, which can be summarized by the partial area under the ROC curve (partial AUC). Early statistical methods for evaluating partial AUC assume that the data are from a specified underlying distribution. Nonparametric estimators of the partial AUC emerged recently, but there are theoretical issues to be addressed. In this dissertation, we propose two new nonparametric statistics, partially integrated ROC and partially integrated weighted ROC, for estimating partial AUC. We show that our partially integrated ROC statistic is a consistent estimator of the partial AUC, and derive its asymptotic distribution which is distribution free under the null hypothesis. In the partially integrated ROC statistic, when the ROC curve crosses the Uniform distribution function (CDF) and if the partial area evaluated contains the crossing point, or when there are multiple crossing, the partially integrated ROC statistic might not perform well. To address this issue, we propose the partially integrated weighted ROC statistic. This statistic evaluates the partially weighted AUC, where larger weight is given when the ROC curve is above the Uniform CDF and smaller weight is given when the ROC curve is below the Uniform CDF. We show that our partially integrated weighted ROC statistic is a consistent estimator of the partially weighted AUC. We derive its asymptotic distribution which is distribution free under the null hypothesis. We propose to apply our two nonparametric statistics to functional category analysis in microarray experiments. We define the functional category analysis to be the statistical identification of over-represented functional gene categories in a microarray experiment based on differential gene expression. We compare our statistics with existing methods for the functional category analysis both via simulation study and application to a real microarray data, and demonstrate that our two statistics are effective for identifying over-represented functional gene categories. We also emphasize the essential role of the empirical distribution function plots and the ROC curves in the functional category analysis.
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Stacy, Catherine Ann. "Applying mixed-effects receiver operating characteristic (ROC) curve analysis to diagnostic evaluations of human learning." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3035981.

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Contini, Letizia. "Identificazione del danno in strutture reticolari via frequenze di vibrare e Receiver Operating Characteristic (ROC) curve." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il seguente elaborato affronta, nella sua prima parte, gli effetti che vari disturbi esterni ed incontrollabili, possono provocare sui parametri modali della struttura durante il monitoraggio dinamico, portando ad un'errata valutazione della salute di salute dell'opera. Allo scopo di ridurre questo problema, nella seconda parte della tesi, viene analizzata una struttura reticolare in acciaio sottoposta a indice di danno, temperatura e livello di rumore variabile. Tale modello è stato utilizzato per la ricerca di una metrica robusta che, indipendentemente dai disturbi esterni applicati, riesca a fornire una precisa ed esatta identificazione del danno strutturale, e quindi, conseguentemente, una valida valutazione dello stato di conservazione dell'opera. Una volta eseguite tutte le analisi e valutazioni del caso, la relazione determinata è stata applicata a diverse tipologie di strutture, al fine di verificarne la sua validità. Nell'ultima parte dell'elaborato sono state riportate le conclusioni, in cui si è validata l'analisi eseguita e i suoi conseguenti risultati.
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Sun, Fangfang. "Semi-parametric inference for the partial area under the ROC curve." unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-113213/.

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Thesis (M.S.)--Georgia State University, 2008.
Title from file title page. Gengsheng Qin, committee chair; Yu-Sheng Hsu, Yixin Fang, Yuanhui Xiao, committee members. Description based on contents viewed July 22, 2009. Includes bibliographical references (p. 29-30).
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Books on the topic "Receiver Operating Characteristic (ROC) Curve"

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J, Hand D., ed. ROC curves for continuous data. Boca Raton: Chapman & Hall/CRC, 2009.

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Statistical evaluation of diagnostic performance: Topics in ROC analysis. Boca Raton: Taylor & Francis, 2012.

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Signal detection theory and ROC analysis in psychology and diagnostics: Collected papers. Mahwah, N.J: L. Erlbaum Associates, 1996.

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He, Yaohua. Nonparametric methods for receiver operating characteristic (ROC) curve analysis in genomic studies and diagnostic medicine. 2006.

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Zou, Kelly H., Aiyi Liu, Andriy I. Bandos, Lucila Ohno-Machado, and Howard E. Rockette. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis. Taylor & Francis Group, 2016.

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Zou, Kelly H. Statistical Evaluation of Diagnostic Performance: Topics in Roc Analysis. Taylor & Francis Group, 2011.

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Zou, Kelly H., Aiyi Liu, Andriy I. Bandos, Lucila Ohno-Machado, and Howard E. Rockette. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis. Taylor & Francis Group, 2016.

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Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Single group studies. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0006.

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Chapter 6 discusses single group studies, and covers prevalence, how to present results, screening studies, calculating, and presenting sensitivity and specificity. It discusses how to deal with calculations with a rare condition where the numbers are small. Finally, it discusses the use of receiver operating characteristic (ROC) curves. The chapter includes analyses using Stata, SAS, SPSS, and R.
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Observer Performance Methods for Diagnostic Imaging: Foundations, Modeling, and Applications with R-Based Examples. Taylor & Francis Group, 2017.

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Chakraborty, Dev P. Observer Performance Methods for Diagnostic Imaging. Taylor & Francis Group, 2021.

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Book chapters on the topic "Receiver Operating Characteristic (ROC) Curve"

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Melo, Francisco. "Receiver Operating Characteristic (ROC) Curve." In Encyclopedia of Systems Biology, 1818–23. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_242.

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Marcus, Pamela M. "Performance Measures." In Assessment of Cancer Screening, 23–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94577-0_3.

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AbstractPerformance measures reflect the link between cancer screening test results and cancer diagnoses. They measure the ability of cancer screening to lead to detection of cancer, and provide no evidence as to screening’s ability to reduce mortality. Performance measures are rarely considered sufficient evidence to implement cancer screening for the first time, though they have driven dissemination of tests that are thought to represent upgrades of established cancer screening tests. Chapter 3 presents the six key performance measures: sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate. Receiver operating characteristic (ROC) curves and area under the curve (AUC), which are calculated from sensitivity and false positive rate, are presented as well. Additional aspects of performance measures, including the role of disease prevalence in cancer screening test performance (in particular, the impact on positive predictive value), are discussed.
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Mas, J. F. "Receiver Operating Characteristic (ROC) Analysis." In Geomatic Approaches for Modeling Land Change Scenarios, 465–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60801-3_30.

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Nockemann, C., G. R. Tillack, H. Wessel, H. Heidt, and V. Konchina. "Receiver Operating Characteristic (ROC) in Nondestructive Inspection." In Advances in Signal Processing for Nondestructive Evaluation of Materials, 13–30. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1056-3_2.

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da Cunha, Daniela Ferreira, and Ana Cristina Braga. "Receiver Operating Characteristic (ROC) Packages Comparison in R." In Computational Science and Its Applications – ICCSA 2017, 545–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62395-5_37.

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Wijaya, I. Putu Krishna, Peeranan Towashiraporn, Anish Joshi, Susantha Jayasinghe, Anggraini Dewi, and Md Nurul Alam. "Climate Change-Induced Regional Landslide Hazard and Exposure Assessment for Aiding Climate Resilient Road Infrastructure Planning: A Case Study in Bagmati and Madhesh Provinces, Nepal." In Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022, 175–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16898-7_12.

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AbstractNepal’s hilly and mountainous regions are highly susceptible to landslides triggered by extreme precipitations. The prevalence of such landslides has increased due to climate change-induced extreme hydro-meteorological conditions. These recurring landslides have significantly impacted the road transport infrastructure, which is the economic lifeline for cities and socio-economic mobility of rural communities in the hilly and mountainous regions of the country. This study modelled extreme rainfall scenarios for the current 1976–2005 baseline and future horizons of 2030, 2050, and 2080 to develop high-resolution 1 km × 1 km mean precipitation datasets under RCP4.5 and RCP8.5. Based on these extreme precipitation scenarios, we developed high-resolution landslide hazard models adopting integrated weighted index by combining the Frequency Ratio (FR) and Analytical Hierarchical Process (AHP) methods using multi-variate factors. The multi-variate factors included three terrain parameters—slope, aspect, and elevation; two soil parameters—lithology and soil type; two Euclidean distance parameters from the likely sources—distance from the lineaments and distance from the stream/river; an anthropogenic parameter—land use; and the climate parameter—the mean annual rainfall for four-time horizons and two RCPs. These parameters were spatially modelled and combined using the weighted overlay method to generate a landslide hazard model. As demonstration case studies, the landslide hazard models were developed for Bagmati and Madhesh provinces. The models were validated using the Receiver Operating Characteristic curve (ROC) approach, which showed a satisfactory 81–86% accuracy in the study area. Spatial exposure analysis of the road network assets under the Strategic Road Network (SRN) was completed for seven landslide hazard scenarios. In both Bagmati and Madhesh provinces, the exposure analysis showed that the proportion of road sections exposed to landslide hazard significantly increases for the future climate change scenarios compared to the current baseline scenario.
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Schuckers, Michael E. "Receiver Operating Characteristic Curve and Equal Error Rate." In Information Science and Statistics, 155–204. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-202-5_5.

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Vexler, Albert, and Alan D. Hutson. "A Brief Review of Receiver Operating Characteristic Curve Analyses." In Statistics in the Health Sciences, 185–204. Boca Raton, Florida : CRC Press, [2018]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b21899-7.

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Vexler, Albert, and Jihnhee Yu. "Empirical Likelihood Application to Receiver Operating Characteristic Curve Analysis." In Empirical Likelihood Methods in Biomedicine and Health, 219–46. Boca Raton, Florida : CRC Press, [2018]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781351001526-8.

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Severeyn, Erika, Jesús Velásquez, Héctor Herrera, Sara Wong, and Alexandra La Cruz. "Analysis of Receiver Operating Characteristic Curve Using Anthropometric Measurements for Obesity Diagnosis." In Systems and Information Sciences, 71–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59194-6_7.

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Conference papers on the topic "Receiver Operating Characteristic (ROC) Curve"

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Wu, Tao, Haibin Huang, Guangwei Du, and Yiyong Sun. "A novel partial area index of receiver operating characteristic (ROC) curve." In Medical Imaging, edited by Berkman Sahiner and David J. Manning. SPIE, 2008. http://dx.doi.org/10.1117/12.769888.

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Trickey, Stephen, Mark Seaver, and Jon Nichols. "Quantification of damage detection schemes using receiver operating characteristic (ROC) curves." In The 14th International Symposium on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, edited by H. Felix Wu, Aaron A. Diaz, and Peter J. Shull. SPIE, 2007. http://dx.doi.org/10.1117/12.715210.

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Swensson, Richard G., Jill L. King, Walter F. Good, and David Gur. "Using a constrained formulation based on probability summation to fit receiver operating characteristic (ROC) curves." In Medical Imaging 2000, edited by Elizabeth A. Krupinski. SPIE, 2000. http://dx.doi.org/10.1117/12.383102.

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Davison, Craig R., and Chris Drummond. "Application of Cost Matrices and Cost Curves to Enhance Diagnostic Health Management Metrics for Gas Turbine Engines." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59630.

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Statistically based metrics for gas turbine engine diagnostic systems are required to evaluate competing products fairly and to establish a convincing business case. Diagnostic algorithm validation often includes engine testing with implanted faults. The implantation rate is rarely, if ever, representative of the true fault occurrence rate. A technique is presented to convert a confusion matrix with a non-representative fault distribution to one representative of the expected distribution. The small sample size associated with fault implantation studies requires a confidence interval on the results to provide valid comparisons and a method for calculating them is presented. The use of cost matrices to weight confusion matrices, based on the associated cost of each outcome, is demonstrated. The calculation of metrics on the resulting weighted confusion matrix will measure the relative cost of the diagnostic technique, rather than just its accuracy. In this paper, the generation and use of cost matrices is presented and their application to a hypothetical test case is demonstrated. Receiver operating characteristic (ROC) curves evaluate diagnostic system performance across a range of threshold settings. This allows an algorithm’s ability to be assessed over a range of possible usage. Cost curves are analogous to ROC curves but offer several advantages. The techniques for applying cost curves to diagnostic algorithms are presented and their advantages over ROC curves are outlined.
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Velásquez Pérez, Torcoroma. "Prácticas pedagógicas emergentes." In Nuevas realidades para la educación en ingeniería: currículo, tecnología, medio ambiente y desarrollo. Asociacion Colombiana de Facultades de Ingeniería - ACOFI, 2022. http://dx.doi.org/10.26507/paper.2283.

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Los modelos educativos en la actualidad han perdido vigencia, más cuando la educación se ha enfrentado a una transición de la educación presencial a la educación mediada por tecnologías, para la cual nadie estaba preparado; las secuelas de la pandemia del COVID-19 (SARS-CoV-2), han dejado huellas imborrables en el devenir histórico de la humanidad; los sistemas han tenido que re-inventarse sobre la marcha y ajustar a nuevas estructuras para dar continuidad a los procesos de formación y capacitación; de allí emerge el objetivo del presente aporte: generar un compendio teórico – práctico de pedagogías emergentes y de educación 4.0 para la constitución de un modelo de enseñanza holístico en función al mejoramiento académico de estudiantes universitarios de la facultad de Ingeniería de Sistemas de la Universidad Francisco de Paula Santander Ocaña. Investigación enmarcada en los métodos mixtos, (cuantitativo – cualitativo), desarrollado en tres momentos: primer momento documental, segundo momento entrevista semi-estructurada para docentes y el tercer momento encuesta a estudiantes. Reflejado a través de redes neuronales artificiales (perceptron multicapa) algoritmo de entrenamiento packpropagation; así mismo se empleó la curva ROC (receiver operating characteristic curve) utilizando el software SPSS. Los resultados obtenidos dejan evidenciar que las pedagogías emergentes y la educación 4.0 se convierten en una metodología pedagógica innovadora para el mejoramiento de la calidad de los procesos de enseñanza y aprendizaje enfocados en un modelo de enseñanza holístico.
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Coulter, Cecilia, Paula Baingana, and Pascaline Mukakamari. "Implementing Machine Learning Algorithms to Predict Donor Status: Preliminary Work with Data from an Institution of Higher Learning." In Congreso Internacional de Ingeniería de Sistemas. Universidad de Lima, 2020. http://dx.doi.org/10.26439/ciis2019.5527.

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Identifying potential donors allows institutions of higher learning to conduct more effective fundraising campaigns. Machine learning classification algorithms can be useful in building models to predict donor status. However, when data contains imbalanced classes, like the data we used for this project, models tend to over-index the majority class, which was non-donors in this case. These results have significant implications for institutions in that they may not pursue entities that may, in fact, become donors. In order to improve the usefulness of our model, we used a resampling technique called random undersampling (RUS) to balance the data and also the area under the receiver operating characteristic curve (AUC-ROC) metric to evaluate the performance. Our final model improved its predictive power from 67% to 76%. Institutions of higher learning can use this machine learning model to more efficiently target the pool of potential donors, saving money and time. Future research will focus on improving the predictive accuracy of our model by exploring other data manipulation techniques that minimize the effect of imbalanced data, changing thresholds for classification algorithms, and using genetic programming and feature engineering.
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Figueirêdo, Ilan Sousa, Tássio Farias Carvalho, Wenisten José Dantas Silva, Lílian Lefol Nani Guarieiro, and Erick Giovani Sperandio Nascimento. "Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31297-ms.

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Abstract Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an annotated dataset, this paper intends to perform a comparative evaluation performance of unsupervised learning algorithms to support experts for anomaly detection and pattern recognition in multivariate time-series data. So, the goal is to allow experts to analyze a small set of patterns and label them, instead of analyzing large datasets. This paper used the public 3W database of three offshore naturally flowing wells. The experiment used real data of production of O&G from underground reservoirs with the following anomalous events: (i) spurious closure of Downhole Safety Valve (DHSV) and (ii) quick restriction in Production Choke (PCK). Six unsupervised machine learning algorithms were assessed: Cluster-based Algorithm for Anomaly Detection in Time Series Using Mahalanobis Distance (C-AMDATS), Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest (RRCF). The comparison evaluation of unsupervised learning algorithms was performed using a set of metrics: accuracy (ACC), precision (PR), recall (REC), specificity (SP), F1-Score (F1), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision-Recall Curve (AUC-PRC). The experiments only used the data labels for assessment purposes. The results revealed that unsupervised learning successfully detected the patterns of interest in multivariate data without prior annotation, with emphasis on the C-AMDATS algorithm. Thus, unsupervised learning can leverage supervised models through the support given to data annotation.
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Mariani, Stefano, Thompson V. Nguyen, Xuan Zhu, Simone Sternini, Francesco Lanza di Scalea, Mahmood Fateh, and Robert Wilson. "Non-Contact Ultrasonic Guided Wave Inspection of Rails: Next Generation Approach." In 2016 Joint Rail Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/jrc2016-5771.

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The University of California at San Diego (UCSD), under a Federal Railroad Administration (FRA) Office of Research and Development (R&D) grant, is developing a system for high-speed and non-contact rail defect detection. A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection, paired with a real-time statistical analysis algorithm, has been realized. This system requires a specialized filtering approach based on electrical impedance matching due to the inherently poor signal-to-noise ratio of air-coupled ultrasonic measurements in rail steel. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. The system’s operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. The prototype based on this technology was tested in October 2014 at the Transportation Technology Center (TTC) in Pueblo, Colorado, and again in November 2015 after incorporating changes based on lessons learned.
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Rabinovich, Iris, Isabela Chaves Monteiro Soares, Julia Mizrahi Jakobson, Eduardo Milleo Kac, and Bruno Luiz Zonta. "PREDICTIVE VARIABLES OF PATHOLOGIC COMPLETE RESPONSE (PCR) IN INVASIVE DUCTAL CARCINOMA." In Scientifc papers of XXIII Brazilian Breast Congress - 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s1054.

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Introduction: Achieving a pCR after neoadjuvant chemotherapy is generally associated with better outcomes. Identifying modifiable and non-modifiable variables that correlate with a complete response is important to treatment. Objectives: The aim of this study was to identify clinical and immunohistochemical variables that correlate with pCR, in a public and a private breast cancer unit in the city of Curitiba, state of Paraná, Brazil. Methods: A retrospective analysis was carried out through medical records at Hospital de Clínicas- UFPR and Centro de Doenças da Mama- Hospital Nossa Senhora das Graças. Patients who underwent surgical treatment after neoadjuvant chemotherapy, in the period from January 1, 2017 to December 31, 2020, due to an invasive ductal carcinoma were selected. Clinical and immunohistochemical variables were correlated to the presence of pCR. Ethical committee approval n. 4,295,049. Results: In 240 cases included, 66 (27.5%) reached pCR. There was a significant correlation between pCR and the negativity of the hormonal receptors (p=0.002), HER2 positivity (p=0.002), Ki67 expression (p=0.012) and molecular subtypes (p=0.001). A ROC (receiver operating characteristic) curve analysis identified a 50% percentage as the best cut-off value to predict complete response. On the other hand, it was not observed a significant correlation between pCR and body mass index, or physical activity. Conclusions: There is a strong correlation between immunohistochemical parameters and pCR. Further studies are needed to determine the correlation between modifiable variables.
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Mariani, Stefano, Thompson V. Nguyen, Francesco Lanza di Scalea, and Mahmood Fateh. "High Speed Non-Contact Ultrasonic Guided Wave Inspections of Rails." In 2014 Joint Rail Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/jrc2014-3745.

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This paper describes a new system for high-speed and non-contact rail defect detection being developed at the University of California at San Diego (UCSD). A prototype using an ultrasonic air-coupled guided wave signal generation and air-coupled signal detection has been tested at the UCSD Rail Defect Farm. This solution presents an improvement over the previously considered laser/air-coupled hybrid system because it replaces the costly and hard-to-maintain laser with a much cheaper, faster, and easier-to-maintain air-coupled transmitter. In addition to a real-time statistical analysis algorithm, the prototype uses a specialized filtering approach to mitigate the inherently poor signal-to-noise ratio of the air-coupled ultrasonic measurements in rail steel. The laboratory results indicate that the prototype is able to detect internal rail defects with a high reliability. Various aspects of the prototype have been designed with the aid of numerical analyses. In particular, simulations of ultrasonic guided wave propagation in rails have been performed using a Local Interaction Simulation Approach (LISA) algorithm. Many of the system operating parameters were selected based on Receiver Operating Characteristic (ROC) curves, which provide a quantitative manner to evaluate different detection performances based on the trade-off between detection rate and false positive rate. Extensions of the system capability are planned to add rail surface characterization to the internal rail defect detection to optimize rail grinding operations.
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Reports on the topic "Receiver Operating Characteristic (ROC) Curve"

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Sullivan, Keith M. Global Receiver Operating Characteristic (ROC) Curves for Three Network Topologies. Fort Belvoir, VA: Defense Technical Information Center, March 2003. http://dx.doi.org/10.21236/ada636523.

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Candy, J., and E. Breitfeller. Receiver Operating Characteristic (ROC) Curves: An Analysis Tool for Detection Performance. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1093414.

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Qin, Ling, Qiyu Liu, Hui Wang, and Lipeng Sun. Accuracy of ultrasound in distinguishing pathology of malignant thyroid diseases: A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2021. http://dx.doi.org/10.37766/inplasy2021.12.0072.

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Review question / Objective: This meta-analysis aimed to determine the accuracy of ultrasound in distinguishing pathology of malignant thyroid diseases. Eligibility criteria: Type of study. This study will only include high quality clinical cohort or case control studies. Type of patients. The patients should be those who had undergone breast diseases. Intervention and comparison. This study compares AI with pathology for diagnosing breast diseases. Type of outcomes. The primary outcomes include sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and the area under the curve of the summary receiver operating characteristic.
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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