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Journal articles on the topic 'Auc-Roc'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Hong, Chong Sun, and So Yeon Choi. "ROC curve generalization and AUC." Journal of the Korean Data And Information Science Society 31, no. 4 (2020): 477–88. http://dx.doi.org/10.7465/jkdi.2020.31.4.477.

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Hong, Chong Sun, and Dae Soon Yang. "ROC curve and AUC for linear growth models." Journal of the Korean Data and Information Science Society 26, no. 6 (2015): 1367–75. http://dx.doi.org/10.7465/jkdi.2015.26.6.1367.

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Минин, А. С. "Бинаризация вероятностного прогноза методом ROC AUC". ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 104, № 14 (2023): 87–91. http://dx.doi.org/10.18411/trnio-12-2023-789.

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В работе проведено исследование влияния порога бинаризации вероятностного прогноза классификатора k-ближайших соседей на значение метрики ROC AUC. Путем варьирования порога бинаризации прогнозов и расчета ROC AUC выявлен оптимальный порог, при котором достигается максимальное значение метрики. Актуальность работы обусловлена широким практическим применением вероятностных классификаторов и необходимостью преобразования их непрерывных прогнозов в дискретные классы. Целью исследования является нахождение оптимального значения порога бинаризации для конкретного классификатора и набора данных на ос

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Krupinski, Elizabeth A. "Evaluating AI Clinically—It’s Not Just ROC AUC!" Radiology 298, no. 1 (2021): 47–48. http://dx.doi.org/10.1148/radiol.2020203782.

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Mukhametshin, Rustam F., Olga P. Kovtun, and Nadezhda S. Davydova. "Respiratory parameters as a predictor of hospital outcomes in newborns requiring medical evacuation." Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care 12, no. 4 (2023): 441–52. http://dx.doi.org/10.17816/psaic1292.

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BACKGROUND: Assessment of the clinical condition, prediction of risks and possible outcomes during the transfer of newborns remains an important part of the work of transport teams. Respiratory disorders remain a significant indication for transfer to medical organizations of a higher level of care. AIM: To study the predictive value of the parameters of respiratory support in newborns requiring medical evacuation for the outcomes of treatment. MATERIALS AND METHODS: The observational, cohort, retrospective study included data from neonatal to patients on ventilators (286 newborns)

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Mahmud, Fuad, Badruddowza Badruddowza, Md Shohail Uddin Sarker, et al. "ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION." American Journal of Engineering and Technology 06, no. 10 (2024): 86–99. http://dx.doi.org/10.37547/tajet/volume06issue10-10.

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This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model fo

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Kim, Seong-Jin, Xue-Cheng Jin, Rajaraman Bharanidharan, and Na-Yeon Kim. "Monitoring Multiple Behaviors in Beef Calves Raised in Cow–Calf Contact Systems Using a Machine Learning Approach." Animals 14, no. 22 (2024): 3278. http://dx.doi.org/10.3390/ani14223278.

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The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acce

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Muschelli, John. "ROC and AUC with a Binary Predictor: a Potentially Misleading Metric." Journal of Classification 37, no. 3 (2019): 696–708. http://dx.doi.org/10.1007/s00357-019-09345-1.

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Abbas, Adeel, Farkhanda Abbas, Fazila Abbas, Abdulwahed Fahad Alrefaei, and Mohammed Fahad Albeshr. "Enhancing Landslide Prediction: A Comparative Study of Ensembled and Non-Ensembled Machine Learning Approaches with Dimensionality Reduction and Random Feature Selection to Showcase Entropy Management." Journal of Sensor Networks and Data Communications 4, no. 3 (2024): 01–23. https://doi.org/10.33140/jsndc.04.03.04.

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This research looks at how well different ensembled and non-ensembled machine learning algorithms perform both before and after dimensionality reduction and manual feature engineering using random feature selection. LightGBM, Extra Trees (EXT), XGBoost, Gradient Boosting Machine (GBM), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree (DT) are among the algorithms that were assessed. With a computational time (CT) of 15.985 seconds prior to dimensionality reduction, LightGBM obtained an AUC/ROC score of 0.833, whereas Extra Trees (EXT), XGBoost, and GBM each ob

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Vishnu, Vardhan R*, and S. Balaswamy. "COMPARING SEVERAL DIAGNOSTIC PROCEDURES USING THE INTRINSIC MEASURES OF ROC CURVE." Indian Journal of Medical Research and Pharmaceutical Sciences 3, no. 3 (2016): 48–55. https://doi.org/10.5281/zenodo.47521.

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Comparison of diagnostic tests is essential in medicine. Test procedures for comparing two or more ROC curves are all based on measures d<sup>'</sup>, AUC and the maximum likelihood estimates of binormal ROC curves. However, intrinsic measures such as sensitivity and specificity also play a pivotal role in assessing the performance of several diagnostic procedures. In this paper, a new methodology is proposed in order to compare several diagnostic procedures using the intrinsic measures of ROC curve

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Khaidarov, A. G., A. I. Soloviev, and D. A. Budko. "STUDY OF THE MOST EFFICIENT MODELS AND ATRIBUTION ALGORITHMS USING THE ROC AUC INDICATOR." Современные наукоемкие технологии (Modern High Technologies), no. 7 2022 (2022): 63–68. http://dx.doi.org/10.17513/snt.39234.

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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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García de Guadiana-Romualdo, Luis, María Dolores Albaladejo-Otón, Mario Berger, et al. "Prognostic performance of pancreatic stone protein in critically ill patients with sepsis." Biomarkers in Medicine 13, no. 17 (2019): 1469–80. http://dx.doi.org/10.2217/bmm-2019-0174.

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Aim: To assess the prognostic value for 28-day mortality of PSP in critically ill patients with sepsis. Material & methods: 122 consecutive patients with sepsis were enrolled in this study. Blood samples were collected on admission and day 2. Results: On admission, the combination of PSP and lactate achieved an area under the receiver operating characteristic (AUC-ROC) of 0.796, similar to sequential organ failure assessment score alone (AUC-ROC: 0.826). On day 2, PSP was the biomarker with the highest performance (AUC-ROC: 0.844), although lower (p = 0.041) than sequential organ failure a

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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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Sauka, Kudzai, Gun-Yoo Shin, Dong-Wook Kim, and Myung-Mook Han. "Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning." Applied Sciences 12, no. 13 (2022): 6451. http://dx.doi.org/10.3390/app12136451.

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The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is due to deep neural networks (DNN) complexity and efficiency in making anomaly detection activities more accurate. However, the complexity of these models makes them black-box models, as they lack explainability and interpretability. Not only is the DNN perceived as a black-box model, but recent research evidence has also shown that they are vu

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Amala, R., and Sudesh Pundir. "ROC Curve and AUC for A Left-Truncated Sample from Rayleigh Distribution." American Journal of Mathematical and Management Sciences 34, no. 2 (2014): 89–116. http://dx.doi.org/10.1080/01966324.2014.969461.

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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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So, Yoon Kyoung, Zero Kim, Taek Yoon Cheong, et al. "Detection of Cancer Recurrence Using Systemic Inflammatory Markers and Machine Learning after Concurrent Chemoradiotherapy for Head and Neck Cancers." Cancers 15, no. 14 (2023): 3540. http://dx.doi.org/10.3390/cancers15143540.

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Pretreatment values of the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are well-established prognosticators in various cancers, including head and neck cancers. However, there are no studies on whether temporal changes in the NLR and PLR values after treatment are related to the development of recurrence. Therefore, in this study, we aimed to develop a deep neural network (DNN) model to discern cancer recurrence from temporal NLR and PLR values during follow-up after concurrent chemoradiotherapy (CCRT) and to evaluate the model’s performance compared with co

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Shakhmatova, O. O., A. L. Komarov, U. P. Ergasheva, et al. "Which scale is best to assess the risk of upper gastrointestinal bleeding in patients with stable coronary artery disease in the Russian population?" Cardiovascular Therapy and Prevention 23, no. 4 (2024): 3915. http://dx.doi.org/10.15829/1728-8800-2024-3915.

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Aim. To evaluate and compare the prognostic value of various scales on the risk of upper gastrointestinal bleeding (GIB) in patients with stable coronary artery disease (CAD) in the Russian population.Material and methods. The study included patients with stable CAD — participants of the REGATTA-1 prospective single-center registry. The number of points was assessed according to the reference score of the 2015 European Society of Cardiology (ESC), PRECISE-DAPT, ABC-HBR and REACH scores, as well as two Russian scores — the REGATTA score and the ORACUL score, originally developed for patients wi

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Afrianto, Mochammad Agus, and Meditya Wasesa. "Booking Prediction Models for Peer-to-peer Accommodation Listings using Logistics Regression, Decision Tree, K-Nearest Neighbor, and Random Forest Classifiers." Journal of Information Systems Engineering and Business Intelligence 6, no. 2 (2020): 123. http://dx.doi.org/10.20473/jisebi.6.2.123-132.

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Background: Literature in the peer-to-peer accommodation has put a substantial focus on accommodation listings' price determinants. Developing prediction models related to the demand for accommodation listings is vital in revenue management because accurate price and demand forecasts will help determine the best revenue management responses.Objective: This study aims to develop prediction models to determine the booking likelihood of accommodation listings.Methods: Using an Airbnb dataset, we developed four machine learning models, namely Logistics Regression, Decision Tree, K-Nearest Neighbor

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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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Otsuka, Wataru, Shuhei Yoshida, Nanami Taketomi, Yasushi Orihashi, and Isao Koshima. "The Role of Bioelectrical Impedance Analysis in Predicting Secondary Surgical Interventions for Lymphedema." Journal of Clinical Medicine 14, no. 7 (2025): 2151. https://doi.org/10.3390/jcm14072151.

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Background: Bioelectrical impedance analysis (BIA), known for its utility in monitoring fluid balance and lymphedema progression, is non-invasive and practical. However, circumferential tape measurements remain the gold standard for assessing limb volume changes, despite operator variability. This study investigated whether BIA could reliably assess the need for secondary surgical interventions in lymphedema patients. Methods: We retrospectively analyzed lower extremity lymphedema patients who underwent multiple lymphaticovenous anastomoses on both legs from April 2017 to June 2023. This study

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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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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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Mikula, Anthony L., Seth K. Williams, and Paul A. Anderson. "The use of intraoperative triggered electromyography to detect misplaced pedicle screws: a systematic review and meta-analysis." Journal of Neurosurgery: Spine 24, no. 4 (2016): 624–38. http://dx.doi.org/10.3171/2015.6.spine141323.

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OBJECT Insertion of instruments or implants into the spine carries a risk for injury to neural tissue. Triggered electromyography (tEMG) is an intraoperative neuromonitoring technique that involves electrical stimulation of a tool or screw and subsequent measurement of muscle action potentials from myotomes innervated by nerve roots near the stimulated instrument. The authors of this study sought to determine the ability of tEMG to detect misplaced pedicle screws (PSs). METHODS The authors searched the US National Library of Medicine, the Web of Science Core Collection database, and the Cochra

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Zhang, Feng, Song Qiao, Ning Yao, et al. "Anastomotic Rings and Inflammation Values as Biomarkers for Leakage of Stapled Circular Colorectal Anastomoses." Diagnostics 12, no. 12 (2022): 2902. http://dx.doi.org/10.3390/diagnostics12122902.

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Reliable markers to predict or diagnose anastomotic leakage (AL) of stapled circular anastomoses following colorectal resections are an important clinical need. Here, we aim to quantitatively investigate the morphology of anastomotic rings as an early available prognostic marker for AL and compare them to established inflammatory markers. We perform a prospective single-center cohort study, including patients undergoing stapled circular anastomosis between August 2020 and August 2021. The predictive value of the anastomotic ring configuration and the neutrophil-to-lymphocyte ratio (NLR) regard

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Muñoz Martín, Andrés J., María Carmen Viñuela Benéitez, Claudia Iglesias Pérez, et al. "External validation of a bleeding risk assessment model developed with natural language processing and machine learning (PREDICT-AI study) in patients with anticoagulated cancer with venous thromboembolism in the TESEO international prospective registry." Journal of Clinical Oncology 42, no. 16_suppl (2024): e24123-e24123. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e24123.

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e24123 Background: The risk factors for bleeding during anticoagulation in patients with cancer-associated thrombosis (CAT) remain largely unexplored. The recently developed PredictAI model ( Muñoz Martín AJ et al. JCO 40, e18744-e18744(2022) was aimed to predict major bleeding (MB) in anticoagulant-treated cancer patients within the first 6 months following venous thromboembolism (VTE) diagnosis. The goal was to validate the PredictAI model using an independent cohort of patients from the TESEO international, observational, prospective registry. Methods: The performance of the three predictiv

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Dashina, P., and R. Vishnu Vardhan. "ESTIMATION OF AUC OF BI-GENERALIZED EXPONENTIAL ROC CURVE AND ITS ASYMPTOTIC RESULTS." Advances and Applications in Statistics 79 (August 3, 2022): 105–19. http://dx.doi.org/10.17654/0972361722062.

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Gupta, Sushrut, Hardeep Bariar, Santosh Kumar Pandey, and Medhavi Sharma. "Novel Biomarkers for Early Detection of Acute Kidney Injury: A Multi-center Prospective Study." Journal of Neonatal Surgery 14, no. 10S (2025): 709–22. https://doi.org/10.52783/jns.v14.2908.

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Background: Acute kidney injury (AKI) is a common and serious clinical condition associated with high morbidity, mortality, and healthcare costs. Traditional diagnostic markers such as serum creatinine and urine output demonstrate limited sensitivity and specificity for early AKI detection, delaying diagnosis and potentially missing the therapeutic window for effective intervention. This study aimed to evaluate the performance of novel biomarkers, individually and in combination, for early AKI detection across diverse clinical settings. Methods: In this prospective multi-center study, we enrol

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Hakim, Arif Rahman, Windu Gata, Alda Zevana Putri Widodo, Oky Kurniawan, and Arief Rama Syarif. "Analisis Perbandingan Algoritma Machine Learning Terhadap Sentimen Analis Pemindahan Ibu Kota Negara." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 7, no. 2 (2023): 179–85. http://dx.doi.org/10.35870/jtik.v7i2.701.

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Bangsa indonesia diramaikan dengan berita tentang pemindahan Ibu Kota Negara (IKN). Rencana pemerintah memindahkan IKN beralaskan pada visi misi Indonesia tahun 2045 yaitu Indonesia maju. Twitter menjadi salah satu alat komunikasi microblogging yang digunakan untuk menyampaikan opini. Berbagai algoritma telah digunakan untuk menganalisa sentimen terhadap suatu opini seperti Support Vector Machine, Naive Bayes dan Random Forest. Penelitian ini bertujuan untuk membandingkan kinerja tiga algoritma klasifikasi yaitu Support Vector Machine, Naïve Bayes dan Random Fores. Hasil akurasi tertinggi meng

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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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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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Castagno, Simone, Mark Birch, Mihaela van der Schaar, and Andrew McCaskie. "A PRECISION HEALTH APPROACH FOR OSTEOARTHRITIS: PREDICTION OF RAPID KNEE OSTEOARTHRITIS PROGRESSION USING AUTOMATED MACHINE LEARNING." Orthopaedic Proceedings 105-B, SUPP_16 (2023): 23. http://dx.doi.org/10.1302/1358-992x.2023.16.023.

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AbstractIntroductionPrecision health aims to develop personalised and proactive strategies for predicting, preventing, and treating complex diseases such as osteoarthritis (OA), a degenerative joint disease affecting over 300 million people worldwide. Due to OA heterogeneity, which makes developing effective treatments challenging, identifying patients at risk for accelerated disease progression is essential for efficient clinical trial design and new treatment target discovery and development.ObjectivesThis study aims to create a trustworthy and interpretable precision health tool that predic

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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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Owen, Julian A., Matthew B. Fortes, Saeed Ur Rahman, Mahdi Jibani, Neil P. Walsh, and Samuel J. Oliver. "Hydration Marker Diagnostic Accuracy to Identify Mild Intracellular and Extracellular Dehydration." International Journal of Sport Nutrition and Exercise Metabolism 29, no. 6 (2019): 604–11. http://dx.doi.org/10.1123/ijsnem.2019-0022.

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Identifying mild dehydration (≤2% of body mass) is important to prevent the negative effects of more severe dehydration on human health and performance. It is unknown whether a single hydration marker can identify both mild intracellular dehydration (ID) and extracellular dehydration (ED) with adequate diagnostic accuracy (≥0.7 receiver-operating characteristic–area under the curve [ROC-AUC]). Thus, in 15 young healthy men, the authors determined the diagnostic accuracy of 15 hydration markers after three randomized 48-hr trials; euhydration (water 36 ml·kg−1·day−1), ID caused by exercise and

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Muhammad Mufti Sofyanoor, Yunita Widyastuti, Juni Kurniawaty, and Djayanti Sari. "Validity of Acute Physiology and Chronic Health Evaluation (APACHE) IV for the Prediction of Prolonged Intensive Care Unit (ICU) Length of Stay in Dr. Sardjito General Hospital in the COVID Era." Journal of Anesthesiology and Clinical Research 4, no. 2 (2023): 426–33. http://dx.doi.org/10.37275/jacr.v4i2.302.

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Introduction: APACHE IV was a good predictor of ICU length of stay in the USA and some countries outside the USA but poor in others. It is important to develop a scoring system for the Indonesian population, especially in this scope, Dr. Sardjito General Hospital. To develop such a scoring system, it is reasonable to study the validity of APACHE IV in ICU Dr. Sardjito General Hospital for predicting prolonged length of stay. Methods: A retrospective cohort observational study using data from January 1st, 2020, to December 31st, 2020, taken from the ICU of Dr. Sardjito General Hospital. T

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Mohammadi, Mehrdad, Barbara L. McFarlin, Michelle Villegas-Downs, Aiguo Han, Douglas G. Simpson, and William D. O'Brien. "Quantitative ultrasound for preterm birth risk prediction—Part 1: Statistical evaluation." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A351. http://dx.doi.org/10.1121/10.0019123.

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Hypothesis: Predicting the spontaneous preterm birth (sPTB) risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared to using only HC data. HC data defined herein includebirth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment, and physical examination data. Study population included 248 full-term births (FTBs) and 26 sPTBs. Advanced statistical analyses were performed for supervised classification containing 53 scaled candidate features (48 QUS, 5 HC) using nes

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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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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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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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Arcaro, Marina, Chiara Fenoglio, Maria Serpente, et al. "A Novel Automated Chemiluminescence Method for Detecting Cerebrospinal Fluid Amyloid-Beta 1-42 and 1-40, Total Tau and Phosphorylated-Tau: Implications for Improving Diagnostic Performance in Alzheimer’s Disease." Biomedicines 10, no. 10 (2022): 2667. http://dx.doi.org/10.3390/biomedicines10102667.

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Recently, a fully automated instrument for the detection of the Cerebrospinal Fluid (CSF) biomarker for Alzheimer’s disease (AD) (low concentration of Amyloid-beta 42 (Aβ42), high concentration of total tau (T-tau) and Phosphorylated-tau (P-tau181)), has been implemented, namely CLEIA. We conducted a comparative analysis between ELISA and CLEIA methods in order to evaluate the analytical precision and the diagnostic performance of the novel CLEIA system on 111 CSF samples. Results confirmed a robust correlation between ELISA and CLEIA methods, with an improvement of the accuracy with the new C

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Prairi, Muhammad Zirlda, Zurnan Alfian, and Kecitaan Harefa. "Kecocokan Keputusan Pohon Algoritma pada Kimia Organik: Perbandingan ROC AUC Keputusan Pohon dan Ketetanggaan." Journal of Innovative and Creativity (Joecy) 5, no. 2 (2025): 10689–700. https://doi.org/10.31004/joecy.v5i2.1649.

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Kehadiran Machine Learning (ML) di ruang lingkup komputasi modern telah menyebabkan banyak permasalahan dan solusi, terutama pembahasan algoritma. Penelitian ini menganalisa dan eksplorasi efektifitas lima algoritma ML dalam mengklasifikasi asam amino lazim esensial pada protein berdasarkan strukur molekul dan jumlah atom. Dataset diambil secara manual dari buku kimia organik klasik, yang dikonversi dari rumus dan gambar struktur senyawa asam amino lazim menjadi fitur numerik seperti jumlah atom karbon, hidrogen, nitrogen, oksigen, dan sulfur. Kelima algoritma ML yang dianalisis yakni Decision

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Masuda, Jun, Hideo Wada, Takashi Kato, et al. "Enhanced Hypercoagulability Using Clot Waveform Analysis in Patients with Acute Myocardial Infarction and Acute Cerebral Infarction." Journal of Clinical Medicine 13, no. 23 (2024): 7181. http://dx.doi.org/10.3390/jcm13237181.

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Background: Routine activated partial thromboplastin time (APTT) and prothrombin time (PT) measurements do not indicate hypercoagulability in patients with acute myocardial infarction (AMI) and acute cerebral infarction (ACI). Methods: Hypercoagulability in patients with AMI or ACI was evaluated using a clot waveform analysis of the APTT or a small amount of tissue factor activation assay (sTF/FIXa). In the CWA, the derivative peak time (DPT), height (DPH), width (DPW), and area the under the curve (AUC) were evaluated. Results: The APTT did not indicate hypercoagulability, but the second DPT

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Zhou, Allen S., Anthony A. Prince, Alice Z. Maxfield, and Jennifer J. Shin. "Psychological Status as an Effect Modifier of the Association Between Sinonasal Instrument and Imaging Results." Otolaryngology–Head and Neck Surgery 163, no. 5 (2020): 1044–54. http://dx.doi.org/10.1177/0194599820926129.

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Objective: To determine whether psychological status is an effect modifier of the previously observed low discriminatory capacity of Sinonasal Outcome Test-22 (SNOT-22) scores for Lund-Mackay computed tomography (CT) results. Study Design: Observational outcomes study. Setting: Tertiary care center. Subjects and Methods: We assessed patients presenting with chronic sinonasal complaints who underwent CT of the sinuses within 1 month of completing the SNOT-22 instrument. SNOT-22 overall and domain scores were calculated, as were Lund-Mackay CT scores. The discriminatory capacity of SNOT-22 score

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Yang, Zixuan. "Prediction of Autism Spectrum Disorder: Comparison and Tuning of Machine Learning Models." Lecture Notes in Education Psychology and Public Media 35, no. 1 (2024): 1–6. http://dx.doi.org/10.54254/2753-7048/35/20232015.

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The early diagnosis in Autism Spectrum Disorder (ASD) is crucial for timely interventions to address the patients attentional and social challenges. The currently study aims to use machine learning algorithms to accurately predict ASD outcomes. Dataset from a Kaggle competition was used to perform the prediction analysis. Five supervised machine learning algorithms were employed: Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine Classifier (SVC), Random Forest (RF), and Decision Trees (DT). The models were fine-tuned using a range of possible hyperparameters and evalu

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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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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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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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Purnama, Muhammad Adji, Jilang Ramadhani, Yoga Safitra Anugraha, Lusiana Efrizoni, and Rahmaddeni Rahmaddeni. "Perbandingan Performa Algoritma Random Forest dan Gradient Boosting dalam Mengklasifikasi Churn Telco." Techno.Com 23, no. 3 (2024): 645–57. http://dx.doi.org/10.62411/tc.v23i3.11278.

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Customer churn adalah kecenderungan pelanggan berhenti dan berpindah layanan dalam periode tertentu. Ini merupakan masalah utama dalam industri telekomunikasi karena mempengaruhi keuntungan perusahaan. Mempertahankan pelanggan lebih mudah dibandingkan mendapatkan pelanggan baru. Memprediksi churn membantu sektor CRM dalam merancang strategi retensi. Tingkat churn yang tinggi dapat menurunkan pendapatan dan mengganggu stabilitas bisnis. Berdasarkan studi, tingkat churn tahunan di industri telekomunikasi berkisar antara 15% hingga 30%. Data mining, yang memanfaatkan teknik pembelajaran mesin, di

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Yerlikaya, Gülen, Veronica Falcone, Tina Stopp, et al. "To Predict the Requirement of Pharmacotherapy by OGTT Glucose Levels in Women with GDM Classified by the IADPSG Criteria." Journal of Diabetes Research 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/3243754.

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The aim of this study was to assess the association between OGTT glucose levels and requirement of pharmacotherapy in GDM patients classified by the IADPSG criteria. This study included 203 GDM patients (108 managed with lifestyle modification and 95 requiring pharmacotherapy). Clinical risk factors and OGTT glucose concentrations at 0 (G0), 60 (G60), and 120 min (G120) were collected. OGTT glucose levels were significantly associated with the later requirement of pharmacotherapy (ROC-AUC: 71.1, 95% CI: 63.8–78.3). Also, the combination of clinical risk factors (age, BMI, parity, and pharmacot

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