Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19

The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable...

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Bibliographic Details
Published in:	Mathematics (Basel) Vol. 11; no. 4; p. 819
Main Authors:	Štěpánek, Lubomír, Habarta, Filip, Malá, Ivana, Štěpánek, Ladislav, Nakládalová, Marie, Boriková, Alena, Marek, Luboš
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.02.2023
Subjects:	Accuracy Algorithms Antibodies antibody blood level decrease Artificial neural networks Blood Classification Coronaviruses COVID-19 Datasets Decision trees Decomposition Decomposition (Mathematics) Food science Health aspects IgG antibody Laboratories Machine learning machine-learning classification algorithms Methods Model accuracy multivariate logistic regression Performance evaluation Performance prediction Principles Random variables Regression Severe acute respiratory syndrome coronavirus 2 Statistical analysis Statistical models Support vector machines Survival analysis time-to-event variable decomposition time-to-event variable prediction Values Viral antibodies Czech Republic
ISSN:	2227-7390, 2227-7390
Online Access:	Get full text
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