000125775 001__ 125775
000125775 005__ 20241125101134.0
000125775 0247_ $$2doi$$a10.3390/sym15030756
000125775 0248_ $$2sideral$$a133283
000125775 037__ $$aART-2023-133283
000125775 041__ $$aeng
000125775 100__ $$0(orcid)0000-0003-1415-146X$$aAznar-Gimeno, Rocío
000125775 245__ $$aComparing the Min–Max–Median/IQR Approach with the Min–Max Approach, Logistic Regression and XGBoost, maximising the Youden index
000125775 260__ $$c2023
000125775 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125775 5203_ $$aAlthough linearly combining multiple variables can provide adequate diagnostic performance, certain algorithms have the limitation of being computationally demanding when the number of variables is sufficiently high. Liu et al. proposed the min–max approach that linearly combines the minimum and maximum values of biomarkers, which is computationally tractable and has been shown to be optimal in certain scenarios. We developed the Min–Max–Median/IQR algorithm under Youden index optimisation which, although more computationally intensive, is still approachable and includes more information. The aim of this work is to compare the performance of these algorithms with well-known Machine Learning algorithms, namely logistic regression and XGBoost, which have proven to be efficient in various fields of applications, particularly in the health sector. This comparison is performed on a wide range of different scenarios of simulated symmetric or asymmetric data, as well as on real clinical diagnosis data sets. The results provide useful information for binary classification problems of better algorithms in terms of performance depending on the scenario.
000125775 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E46-20R$$9info:eu-repo/grantAgreement/ES/DGA-FSE/IODIDE research group$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00
000125775 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125775 590__ $$a2.2$$b2023
000125775 592__ $$a0.485$$b2023
000125775 591__ $$aMULTIDISCIPLINARY SCIENCES$$b50 / 134 = 0.373$$c2023$$dQ2$$eT2
000125775 593__ $$aChemistry (miscellaneous)$$c2023$$dQ2
000125775 593__ $$aPhysics and Astronomy (miscellaneous)$$c2023$$dQ2
000125775 593__ $$aMathematics (miscellaneous)$$c2023$$dQ2
000125775 593__ $$aComputer Science (miscellaneous)$$c2023$$dQ2
000125775 594__ $$a5.4$$b2023
000125775 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125775 700__ $$0(orcid)0000-0002-3007-302X$$aEsteban, Luis M.
000125775 700__ $$0(orcid)0000-0002-6474-2252$$aSanz, Gerardo$$uUniversidad de Zaragoza
000125775 700__ $$0(orcid)0000-0003-2755-5500$$aHoyo-Alonso, Rafael del
000125775 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000125775 773__ $$g15, 3 (2023), 756 [26 pp.]$$pSymmetry (Basel)$$tSymmetry$$x2073-8994
000125775 8564_ $$s1334499$$uhttps://zaguan.unizar.es/record/125775/files/texto_completo.pdf$$yVersión publicada
000125775 8564_ $$s2713999$$uhttps://zaguan.unizar.es/record/125775/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125775 909CO $$ooai:zaguan.unizar.es:125775$$particulos$$pdriver
000125775 951__ $$a2024-11-22-12:00:09
000125775 980__ $$aARTICLE