Intelligent systems are increasingly being deployed in medicine and healthcare, but there is a need for a robust and objective methodology for evaluating such systems. Potentially, receiver operating characteristic (ROC) analysis could form a basis for the objective evaluation of intelligent medical systems. However, it has several weaknesses when applied to the types of data used to evaluate i...

Robust statistics is an extension of classical parametric statistics that specifically takes into account the fact that the assumed parametric models used by the researchers are only approximate. In this article, we review and outline how robust inferential procedures may routinely be applied in practice in the biomedical research. Numerical illustrations are given for the t-test, regression mo...

In this paper, we propose a new semi-parametric maximum likelihood (ML) estimate of a receiver operating characteristic (ROC) curve that satisfies the property of invariance of the ROC curve and is easy to compute. We show that our new estimator is sqrt[n]-consistent and has an asymptotically normal distribution. Our extensive simulation studies show that the proposed method is efficient and ro...

background: the diagnosis of pulmonary tuberculosis (ptb) is complicated and time-consuming currently. there was association of ptb with serum tumor markers. in this study we aimed to evaluate the predictive role of serum ca125, ca199 and cea as diagnostic tools for ptb. methods: this study was designed as a case-control study with 565 subjects who visited the yijishan hospital from jun to dec ...

In two-group discriminant analysis, the Neyman-Pearson Lemma establishes that the ROC curve for an arbitrary linear function is everywhere below the ROC curve for the true likelihood ratio. The weighted area between these two curves can be used as a risk function for finding good discriminant functions. The weight function corresponds to the objective of the analysis, for example to minimize th...

Lampinen (2016) suggested that proponents of ROC analysis may prefer that approach to the diagnosticity ratio because they are under the impression that it provides a theoretical measure of underlying discriminability (d'). In truth, we and others prefer ROC analysis for applied purposes because it provides an atheoretical measure of empirical discriminability (namely, partial areaunder-the-cur...