ArticleBiometrics2022
Obtaining optimal cutoff values for tree classifiers using multiple biomarkers.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Noninvasive Markers Associated With Portal Hypertension and Varices in Patients With Noncirrhotic Portal Hypertension.Clinical and translational gastroenterology · 2026Article
- Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis.Journal of cellular and molecular medicine · 2025Article
- Interpretable multitemporal liver function indicator model for prediction and risk factor analysis of drug induced liver injury.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre-specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree-structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined.
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