Optimal Cut-Point Selection Methods Under Binary Classification When Subclasses Are Involved.
Pharm Stat
; 2024 Jul 07.
Article
em En
| MEDLINE
| ID: mdl-38972714
ABSTRACT
In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Pharm Stat
Ano de publicação:
2024
Tipo de documento:
Article