125775 20241125101134.0 doi 10.3390/sym15030756 sideral 133283 ART-2023-133283 eng Aznar-Gimeno, Rocío (orcid)0000-0003-1415-146X Comparing the Min–Max–Median/IQR Approach with the Min–Max Approach, Logistic Regression and XGBoost, maximising the Youden index 2023 Access copy available to the general public Unrestricted Although 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. info:eu-repo/grantAgreement/ES/DGA/E46-20R info:eu-repo/grantAgreement/ES/DGA-FSE/IODIDE research group info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.2 2023 MULTIDISCIPLINARY SCIENCES 50 / 134 = 0.373 2023 Q2 T2 0.485 2023 Chemistry (miscellaneous) 2023 Q2 Physics and Astronomy (miscellaneous) 2023 Q2 Mathematics (miscellaneous) 2023 Q2 Computer Science (miscellaneous) 2023 Q2 5.4 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Esteban, Luis M. (orcid)0000-0002-3007-302X Sanz, Gerardo Universidad de Zaragoza (orcid)0000-0002-6474-2252 Hoyo-Alonso, Rafael del (orcid)0000-0003-2755-5500 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 15, 3 (2023), 756 [26 pp.] Symmetry (Basel) Symmetry 2073-8994 1334499 http://zaguan.unizar.es/record/125775/files/texto_completo.pdf Versión publicada 2713999 http://zaguan.unizar.es/record/125775/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125775 articulos driver 2024-11-22-12:00:09 ARTICLE