A Novel Sensitivity Maximization at a Given Specificity Method for Binary Classifications.

ABSTRACT: In the cancer early detection field, logistic regression (LR) is a frequently used approach to establish a combination rule that differentiates cancer from noncancer. However, the application of LR relies on a maximum likelihood approach, which may not yield optimal combination rules for maximizing sensitivity at a clinically desirable specificity and vice versa. In this article, we have developed an improved regression framework, sensitivity maximization at a given specificity (SMAGS), for binary classification that finds the linear decision rule, yielding the maximum sensitivity for a given specificity or the maximum specificity for a given sensitivity. We additionally expand the framework for feature selection that satisfies sensitivity and specificity maximizations. We compare our SMAG

SUBMITTER: Ghasemi SM 

PROVIDER: S-EPMC11875929 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature