Comparing the Min–Max–Median/IQR Approach with the Min–Max Approach, Logistic Regression and XGBoost, maximising the Youden index

Aznar-Gimeno, Rocío ; Esteban, Luis M. ; Sanz, Gerardo (Universidad de Zaragoza) ; Hoyo-Alonso, Rafael del

Resumen: 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.

Idioma: Inglés

DOI: 10.3390/sym15030756

Año: 2023

Publicado en: Symmetry 15, 3 (2023), 756 [26 pp.]
ISSN: 2073-8994

Factor impacto JCR: 2.2 (2023)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 50 / 134 = 0.373 (2023) - Q2 - T2

Factor impacto CITESCORE: 5.4 - Computer Science (miscellaneous) (Q1) - Mathematics (all) (Q1) - Physics and Astronomy (miscellaneous) (Q1) - Chemistry (miscellaneous) (Q2)

Factor impacto SCIMAGO: 0.485 - Chemistry (miscellaneous) (Q2) - Physics and Astronomy (miscellaneous) (Q2) - Mathematics (miscellaneous) (Q2) - Computer Science (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E46-20R
Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/IODIDE research group
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)