pone.0259203.s007.pdf (127.45 kB)
Validation results of the binary classification problem where the positive class is malignant and the negative class is other.
journal contribution
posted on 2021-11-04, 17:35 authored by Alberto Garcia-Zamalloa, Diego Vicente, Rafael Arnay, Arantzazu Arrospide, Jorge Taboada, Iván Castilla-Rodríguez, Urko Aguirre, Nekane Múgica, Ladislao Aldama, Borja Aguinagalde, Montserrat Jimenez, Edurne Bikuña, Miren Begoña Basauri, Marta Alonso, Emilio Perez-TralleroAll training samples have been used except the ones identified as Tuberculous. Area under the curve (AUC), accuracy (Acc), sensitivity (SEN), specificity (SPF) and F1 score (F1) of all the classifiers using three different thresholds: 0.5, the one that maximizes the Youden index in the receiver operating characteristic curve and the one that maximizes the F1 score in the precision-recall curve.
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year prospective multitwo different classificationslow prevalence settinglow prevalence scenariocomposite reference standardroutine biochemical parametersparameters routinely measuredwhite cell countspleural fluid together98 %, whilst97 %, sensitivitypleural effusion includedmalignant may alsomachine learning algorithmspleural fluid combinedpleural fluidparameters included91 %,pleural tuberculosismalignant effusionlearning approachcell countalso allowedxlink ">validation predictionstest probabilitymarch 2013lymphocyte percentagelactate dehydrogenasejune 2020identify effusionsclinical practiceclassifier yieldedclass classifierbest resultbased classifiersadenosine deaminase80 %.
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