{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:45:10Z","timestamp":1785512710384,"version":"3.56.0"},"reference-count":87,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006595","name":"Unitatea Executiva Pentru Finantarea Invatamantului Superior a Cercetarii Dezvoltarii si Inovarii","doi-asserted-by":"publisher","award":["CCCDI-UEFISCDI, project number 101\/2019, COFUND-CHISTERA-SOON, within PNCDI III."],"award-info":[{"award-number":["CCCDI-UEFISCDI, project number 101\/2019, COFUND-CHISTERA-SOON, within PNCDI III."]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators\u2019 values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.<\/jats:p>","DOI":"10.3390\/s22041608","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Method for Data Quality Assessment of Synthetic Industrial Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6254-9291","authenticated-orcid":false,"given":"L\u00e1szl\u00f3 Barna","family":"Iantovics","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2425-6580","authenticated-orcid":false,"given":"C\u0103lin","family":"En\u0103chescu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","unstructured":"Matzka, S. (2021, December 22). AI4I 2020 Predictive Maintenance Dataset. UCI Machine Learning Repository. Available online: www.explorate.ai\/dataset\/predictiveMaintenanceDataset.csv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116807","DOI":"10.1016\/j.apenergy.2021.116807","article-title":"Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence","volume":"291","author":"Chakraborty","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e25097","DOI":"10.2196\/25097","article-title":"Learning the Mental Health Impact of COVID-19 in the United States with Explainable Artificial Intelligence: Observational Study","volume":"8","author":"Jha","year":"2021","journal-title":"JMIR Ment. Health"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Matzka, S. (2020, January 21\u201323). Explainable Artificial Intelligence for Predictive Maintenance Applications. Proceedings of the 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), Irvine, CA, USA.","DOI":"10.1109\/AI4I49448.2020.00023"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, Q.B., Wang, L., Ngan, K.N., Li, H.L., and Meng, F.M. (2019, January 22\u201325). Beyond Synthetic Data: A Blind Deraining Quality Assessment Metric Towards Authentic Rain Image. Proceedings of the 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803329"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.rse.2004.01.014","article-title":"Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data","volume":"90","author":"Kindel","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dell\u2019Amore, L., Villano, M., and Krieger, G. (2019, January 26\u201328). Assessment of Image Quality of Waveform-Encoded Synthetic Aperture Radar Using Real Satellite Data. Proceedings of the 20th International Radar Symposium (IRS), Ulm, Germany.","DOI":"10.23919\/IRS.2019.8768185"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1117\/12.354413","article-title":"Airport-databases for 3D synthetic-vision flight-guidance displays database design, quality-assessment and data generation, Conference on Enhanced and Synthetic Vision 1999","volume":"3691","author":"Friedrich","year":"1999","journal-title":"Proc. SPIE"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Papacharalampopoulos, A., Tzimanis, K., Sabatakakis, K., and Stavropoulos, P. (2020). Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase. Sensors, 20.","DOI":"10.3390\/s20195481"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.saa.2015.03.135","article-title":"Quality assessment of the saffron samples using second-order spectrophotometric data assisted by three-way chemometric methods via quantitative analysis of synthetic colorants in adulterated saffron","volume":"148","author":"Masoum","year":"2015","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.engappai.2007.11.008","article-title":"Online estimation of electric arc furnace tap temperature by using fuzzy neural networks","volume":"21","author":"Cabal","year":"2008","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3451","DOI":"10.1002\/mp.13616","article-title":"Assessment of PET and SPECT phantom image quality through automated binary classification of cold rod arrays","volume":"46","author":"DiFilippo","year":"2019","journal-title":"Med. Phys."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1260\/1756-8277.5.3.201","article-title":"Accuracy assessment of thermoacoustic instability models using binary classification","volume":"5","author":"Hoeijmakers","year":"2013","journal-title":"Int. J. Spray Combust. Dyn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"796502","DOI":"10.1117\/12.878311","article-title":"Causality Analysis of fMRI Data, Conference on Medical Imaging 2011\u2014Biomedical Applications in Molecular, Structural, and Functional Imaging","volume":"7965","author":"Garg","year":"2011","journal-title":"Proc. SPIE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"164386","DOI":"10.1109\/ACCESS.2019.2953104","article-title":"A Simplified Cohen\u2019S Kappa for Use in Binary Classification Data Annotation Tasks","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.saa.2016.05.006","article-title":"Enhancing prediction power of chemometric models through manipulation of the fed spectrophotometric data: A comparative study","volume":"167","author":"Saad","year":"2016","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rymarczyk, T., Kozlowski, E., Klosowski, G., and Niderla, K. (2019). Logistic Regression for Machine Learning in Process Tomography. Sensors, 19.","DOI":"10.3390\/s19153400"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, W.H., Zeng, S., Wu, G.J., Li, H., and Chen, F.F. (2021). Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model. Sensors, 21.","DOI":"10.3390\/s21134384"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ahmed, A., Jalal, A., and Kim, K. (2020). A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression. Sensors, 20.","DOI":"10.3390\/s20143871"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8067","DOI":"10.3390\/s8128067","article-title":"Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression","volume":"8","author":"Mallinis","year":"2008","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9936","DOI":"10.3390\/s120709936","article-title":"A Logistic Regression Model for Predicting Axillary Lymph Node Metastases in Early Breast Carcinoma Patients","volume":"12","author":"Xie","year":"2012","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"276","DOI":"10.21101\/cejph.a4559","article-title":"Active Smoking and Associated Behavioural Risk Factors before and during Pregnancy\u2014Prevalence and Attitudes among Newborns\u2019 Mothers in Mures County, Romania","volume":"24","author":"Ruta","year":"2016","journal-title":"Cent. Eur. J. Public Health"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bouwmeester, W., Zuithoff, N.P., Mallett, S., Geerlings, M.I., Vergouwe, Y., Steyerberg, E.W., Altman, D.G., and Moons, K.G. (2012). Reporting and methods in clinical prediction research: A systematic review. PLoS Med., 9.","DOI":"10.1371\/journal.pmed.1001221"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M14-0698","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and elaboration","volume":"162","author":"Moons","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105587","DOI":"10.1016\/j.clsr.2021.105587","article-title":"Legal aspects of data cleansing in medical AI","volume":"42","author":"Schneeberger","year":"2021","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"139197","DOI":"10.1016\/j.scitotenv.2020.139197","article-title":"Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India","volume":"730","author":"Saha","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1111\/tgis.12739","article-title":"Research on the driving forces of urban hot spots based on exploratory analysis and binary logistic regression model","volume":"25","author":"Cui","year":"2021","journal-title":"Trans. GIS"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Barnieh, B.A., Jia, L., Menenti, M., Jiang, M., Zhou, J., Zeng, Y.L., and Bennour, A. (2021). Modeling the Underlying Drivers of Natural Vegetation Occurrence in West Africa with Binary Logistic Regression Method. Sustainability, 13.","DOI":"10.3390\/su13094673"},{"key":"ref_30","first-page":"10859","article-title":"Injury Severity Level Examination of Pedestrian Crashes: An Application of Binary Logistic Regression","volume":"32","author":"Ozen","year":"2021","journal-title":"Teknik Dergi"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sanchez-Varela, Z., Boullosa-Falces, D., Barrena, J.L.L., and Gomez-Solaeche, M.A. (2021). Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9020139"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Manoharan, H., Teekaraman, Y., Kirpichnikova, I., Kuppusamy, R., Nikolovski, S., and Baghaee, H.R. (2020). Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression. Energies, 13.","DOI":"10.3390\/en13153974"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lopez, A.S.V., and Rodriguez, C.A.M. (2020). Flash Flood Forecasting in Sao Paulo Using a Binary Logistic Regression Model. Atmosphere, 11.","DOI":"10.3390\/atmos11050473"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gonzalez-Betancor, S.M., and Dorta-Gonzalez, P. (2020). Risk of Interruption of Doctoral Studies and Mental Health in PhD Students. Mathematics, 8.","DOI":"10.3390\/math8101695"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tesema, G.A., Seretew, W.S., Worku, M.G., and Angaw, D.A. (2021). Trends of infant mortality and its determinants in Ethiopia: Mixed-effect binary logistic regression and multivariate decomposition analysis. BMC Pregnancy Childbirth, 21.","DOI":"10.1186\/s12884-021-03835-0"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/12460125.2020.1776927","article-title":"Data quality assessment in product failure prediction models","volume":"29","author":"Ferencek","year":"2020","journal-title":"J. Decis. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"977","DOI":"10.2166\/hydro.2015.097","article-title":"Improving predictions made by ANN model using data quality assessment: An application to local scour around bridge piers","volume":"17","author":"Choi","year":"2015","journal-title":"J. Hydroinformatics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e1294","DOI":"10.1002\/widm.1294","article-title":"Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining","volume":"9","author":"Iantovics","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_39","first-page":"1","article-title":"Analysis of Phytoremediation Potential of Crop Plants in Industrial Heavy Metal Contaminated Soil in the Upper Mures River Basin","volume":"31","author":"Morar","year":"2018","journal-title":"J. Environ. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"51","DOI":"10.12700\/APH.10.03.2013.3.5","article-title":"Analysis of linear interpolation of fuzzy sets with entropy-based distances","volume":"10","author":"Joel","year":"2013","journal-title":"Acta Polytech. Hung."},{"key":"ref_41","first-page":"585","article-title":"Anthropometric indices of the newborns related with some lifestyle parameters of women during pregnancy in Tirgu Mures region\u2014A pilot study","volume":"20","author":"Iacob","year":"2018","journal-title":"Prog. Nutr."},{"key":"ref_42","first-page":"80","article-title":"Kinship and Correlation","volume":"4","author":"Galton","year":"2013","journal-title":"Stat. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1001\/jama.2016.7653","article-title":"Logistic Regression Relating Patient Characteristics to Outcomes","volume":"316","author":"Tolles","year":"2016","journal-title":"JAMA"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1097\/00005373-198704000-00005","article-title":"Evaluating trauma care: The TRISS method. Trauma Score and the Injury Severity Score","volume":"27","author":"Boyd","year":"1987","journal-title":"J. Trauma"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/S1072-7515(00)00758-4","article-title":"Prognostic factors for mortality in left colonic peritonitis: A new scoring system","volume":"191","author":"Biondo","year":"2000","journal-title":"J. Am. Coll. Surg."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1097\/00003246-199510000-00007","article-title":"Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome","volume":"23","author":"Marshall","year":"1995","journal-title":"Crit. Care Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1001\/jama.1993.03510240069035","article-title":"A new Simplified Acute Physiology Score (SAPS II) based on a European\/North American multicenter study","volume":"270","author":"Lemeshow","year":"1993","journal-title":"JAMA"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","article-title":"An analysis of variance test for normality (complete samples)","volume":"52","author":"Shapiro","year":"1965","journal-title":"Biometrika"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1093\/biomet\/58.2.341","article-title":"An omnibus test of normality for moderate and large size samples","volume":"58","year":"1971","journal-title":"Biometrika"},{"key":"ref_50","first-page":"21","article-title":"Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests","volume":"2","author":"Razali","year":"2011","journal-title":"J. Stat. Model. Anal."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1080\/00031305.1986.10475419","article-title":"An analytic approximation to the distribution of Lilliefors\u2019s test statistic for normality","volume":"40","author":"Dallal","year":"1986","journal-title":"Am. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1080\/03610920701653094","article-title":"Bringing closure to the plotting position controversy","volume":"37","author":"Makkonen","year":"2008","journal-title":"Commun. Stat. Theory Methods"},{"key":"ref_53","first-page":"1","article-title":"A Technique for the Measurement of Attitudes","volume":"140","author":"Likert","year":"1932","journal-title":"Arch. Psychol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1080\/00401706.1962.10490038","article-title":"Transformation of the Independent Variables","volume":"4","author":"Box","year":"1962","journal-title":"Technometrics"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"429","DOI":"10.2307\/2986270","article-title":"Regression using fractional polynomials of continuous covariates: Parsimonious parametric modeling","volume":"43","author":"Royston","year":"1994","journal-title":"Appl. Stat."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Royston, P., and Sauerbrei, W. (2008). Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables, Wiley.","DOI":"10.1002\/9780470770771"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1002\/(SICI)1097-0258(20000229)19:4<453::AID-SIM350>3.0.CO;2-5","article-title":"What do we mean by validating a prognostic model?","volume":"19","author":"Altman","year":"2000","journal-title":"Stat. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/sim.4780030207","article-title":"Regression modelling strategies for improved prognostic prediction","volume":"3","author":"Harrell","year":"1984","journal-title":"Stat. Med."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Harrell, F.E. (2001). Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, Springer.","DOI":"10.1007\/978-1-4757-3462-1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1177\/0272989X0102100106","article-title":"Prognostic modeling with logistic regression analysis","volume":"21","author":"Steyerberg","year":"2001","journal-title":"Med. Decis. Mak."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Steyerberg, E.W. (2009). Clinical Prediction Models, Springer.","DOI":"10.1007\/978-0-387-77244-8"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","article-title":"Tutorial in biostatistics\u2014Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Stat. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1002\/(SICI)1097-0258(20000430)19:8<1059::AID-SIM412>3.0.CO;2-0","article-title":"Prognostic modelling with logistic regression analysis: A comparison of selection and estimation methods in small data sets","volume":"19","author":"Steyerberg","year":"2000","journal-title":"Stat. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/S0895-4356(03)00047-7","article-title":"Internal and external validation of predictive models: A simulation study of bias and precision in small samples","volume":"56","author":"Steyerberg","year":"2003","journal-title":"J. Clin. Epidemiol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.1002\/sim.1422","article-title":"Simplifying a prognostic model: A simulation study based on clinical data","volume":"21","author":"Ambler","year":"2002","journal-title":"Stat. Med."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1002\/sim.6782","article-title":"Review and evaluation of penalised regression methods for risk prediction in lowdimensional data with few events","volume":"35","author":"Pavlou","year":"2016","journal-title":"Stat. Med."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Moons, K.G., de Groot, J.A., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D.G., Reitsma, J.B., and Collins, G.S. (2014). Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med, 11.","DOI":"10.1371\/journal.pmed.1001744"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"h3868","DOI":"10.1136\/bmj.h3868","article-title":"How to develop a more accurate risk prediction model when there are few events","volume":"351","author":"Pavlou","year":"2015","journal-title":"BMJ"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.jclinepi.2010.11.012","article-title":"Performance of logistic regression modeling: Beyond the number of events per variable, the role of data structure","volume":"64","author":"Courvoisier","year":"2011","journal-title":"J. Clin. Epidemiol."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Van Smeden, M., de Groot, J.A., Moons, K.G., Collins, G.S., Altman, D.G., Eijkemans, M.J., and Reitsma, J.B. (2016). No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Med. Res. Methodol., 16.","DOI":"10.1186\/s12874-016-0267-3"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.jclinepi.2016.02.031","article-title":"Adequate sample size for developing prediction models is not simply related to events per variable","volume":"76","author":"Ogundimu","year":"2016","journal-title":"J. Clin. Epidemiol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1177\/0962280218784726","article-title":"Sample size for binary logistic prediction models: Beyond events per variable criteria","volume":"28","author":"Smeden","year":"2019","journal-title":"Stat. Methods Med. Res."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Fahrmeir, L., Kneib, T., Lang, S., and Marx, B. (2013). Regression: Models, Methods and Applications, Springer.","DOI":"10.1007\/978-3-642-34333-9"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Ward, M.D., and Ahlquist, J.S. (2018). Maximum Likelihood for Social Science: Strategies for Analysis, Cambridge University Press.","DOI":"10.1017\/9781316888544"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., and Lemeshow, S. (2013). Applied Logistic Regression, Wiley. [3rd ed.].","DOI":"10.1002\/9781118548387"},{"key":"ref_76","unstructured":"Cohen, J., Cohen, P., West, S.G., and Aiken, L.S. (2002). Applied Multiple Regression\/Correlation Analysis for the Behavioral Sciences, Routledge. [3rd ed.]."},{"key":"ref_77","unstructured":"Cox, D.D., and Snell, E.J. (1989). The Analysis of Binary Data, Chapman and Hall. [2nd ed.]."},{"key":"ref_78","unstructured":"Allison, P.D. (2014, January 23\u201326). Measures of fit for logistic regression. Proceedings of the SAS Global Forum 2014 Conference, Washington, DC, USA. paper no. 1485\u20132014."},{"key":"ref_79","unstructured":"Long, J.S., and Freese, J. (2014). Regression Models for Categorical Dependent Variables Using Stata, Stata Press. [3rd ed.]."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/TASLP.2015.2409733","article-title":"Maximum F1-Score Discriminative Training Criterion for Automatic Mispronunciation Detection","volume":"23","author":"Huang","year":"2015","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Processing"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.orl.2020.05.012","article-title":"A distributionally robust area under curve maximization model","volume":"48","author":"Ma","year":"2020","journal-title":"Oper. Res. Lett."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.jmp.2004.08.005","article-title":"Symmetric receiver operating characteristics","volume":"48","author":"Killeen","year":"2004","journal-title":"J. Math. Psychol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1002\/ece3.2654","article-title":"Prevalence dependence in model goodness measures with special emphasis on true skill statistics","volume":"7","author":"Somodi","year":"2017","journal-title":"Ecol. Evol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1177\/0013164482421018","article-title":"A Generalized Kappa Coefficient","volume":"42","author":"Uebersax","year":"1982","journal-title":"Educ. Psychol. Meas."},{"key":"ref_85","unstructured":"Dua, D., and Graff, C. (2019). UCI Machine Learning Repository, University of California, School of Information and Computer Science. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1038\/072294b0","article-title":"The Problem of the Random Walk","volume":"72","author":"Pearson","year":"1905","journal-title":"Nature"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-1014-6","article-title":"A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms","volume":"20","author":"Carrington","year":"2020","journal-title":"BMC Med. Inform. Decis. 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