Biomaterial Database

Binary classification using multivariate receiver operating characteristic curve for continuous data. 2016

G Sameera, and R Vishnu Vardhan, and K V S Sarma

a Department of Statistics , Pondicherry University , Pondicherry , India.

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.

UI	MeSH Term	Description	Entries
D008107	Liver Diseases	Pathological processes of the LIVER.	Liver Dysfunction,Disease, Liver,Diseases, Liver,Dysfunction, Liver,Dysfunctions, Liver,Liver Disease,Liver Dysfunctions
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012372	ROC Curve	A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.	ROC Analysis,Receiver Operating Characteristic,Analysis, ROC,Analyses, ROC,Characteristic, Receiver Operating,Characteristics, Receiver Operating,Curve, ROC,Curves, ROC,ROC Analyses,ROC Curves,Receiver Operating Characteristics
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015415	Biomarkers	Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, ENVIRONMENTAL EXPOSURE and its effects, disease diagnosis; METABOLIC PROCESSES; SUBSTANCE ABUSE; PREGNANCY; cell line development; EPIDEMIOLOGIC STUDIES; etc.	Biochemical Markers,Biological Markers,Biomarker,Clinical Markers,Immunologic Markers,Laboratory Markers,Markers, Biochemical,Markers, Biological,Markers, Clinical,Markers, Immunologic,Markers, Laboratory,Markers, Serum,Markers, Surrogate,Markers, Viral,Serum Markers,Surrogate Markers,Viral Markers,Biochemical Marker,Biologic Marker,Biologic Markers,Clinical Marker,Immune Marker,Immune Markers,Immunologic Marker,Laboratory Marker,Marker, Biochemical,Marker, Biological,Marker, Clinical,Marker, Immunologic,Marker, Laboratory,Marker, Serum,Marker, Surrogate,Serum Marker,Surrogate End Point,Surrogate End Points,Surrogate Endpoint,Surrogate Endpoints,Surrogate Marker,Viral Marker,Biological Marker,End Point, Surrogate,End Points, Surrogate,Endpoint, Surrogate,Endpoints, Surrogate,Marker, Biologic,Marker, Immune,Marker, Viral,Markers, Biologic,Markers, Immune