Modelling Receiver Operating Characteristic Curves Using Gaussian Mixtures

TL;DR

This paper introduces a Gaussian mixture model approach for ROC curve modeling, offering greater flexibility and improved performance over traditional binormal methods, especially with atypical data.

Contribution

It proposes using Gaussian mixtures for ROC modeling, enhancing flexibility and accuracy compared to standard binormal models.

Findings

Our method outperforms traditional binormal ROC models.

Monte Carlo simulation effectively addresses the lack of closed-form solutions.

The approach better captures atypical data distributions.

Abstract

The receiver operating characteristic curve is widely applied in measuring the performance of diagnostic tests. Many direct and indirect approaches have been proposed for modelling the ROC curve, and because of its tractability, the Gaussian distribution has typically been used to model both populations. We propose using a Gaussian mixture model, leading to a more flexible approach that better accounts for atypical data. Monte Carlo simulation is used to circumvent the issue of absence of a closed-form. We show that our method performs favourably when compared to the crude binormal curve and to the semi-parametric frequentist binormal ROC using the famous LABROC procedure.