title: Binary classification using multivariate receiver operating characteristic curve for continuous data.
abstract: The classification scenario needs handling of more than one biomarker. The main objective of the work is to propose a multivariate receiver operating characteristic (MROC) model which linearly combines the markers to classify them into one of the two groups and also to determine an optimal cut point. Simulation studies are conducted for four sets of mean vectors and covariance matrices and a real dataset is also used to demonstrate the proposed model. Linear and quadratic discriminant analysis has also been applied to the above datasets in order to explain the ease of the proposed model. Bootstrapped estimates of the parameters of the ROC curve are also estimated.
authors.affiliation: a Department of Statistics , Pondicherry University , Pondicherry , India.
authors.forename: G
authors.initials: G
authors.lastname: Sameera
authors.affiliation: a Department of Statistics , Pondicherry University , Pondicherry , India.
authors.forename: R Vishnu
authors.initials: RV
authors.lastname: Vardhan
authors.affiliation: b Department of Statistics , Sri Venkateswara University , Tirupati , India.
authors.forename: K V S
authors.initials: KV
authors.lastname: Sarma
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis; D015233:Models, Statistical; D012372:ROC Curve
chemical_list: D015415:Biomarkers
keywords: AUC; MROC; Optimal cutoff