The use of receiver operating characteristic curves in biomedical informatics

Authors:
Thomas A. Lasko;Jui G. Bhagwat;Kelly H. Zou;Lucila Ohno-Machado
Affiliations:
Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology;Department of Radiology, Brigham and Women's Hospital;Department of Radiology, Brigham and Women's Hospital and Department of Health Care Policy, Harvard Medical School;Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA and Department of Radiology, Brigham and Women's Hospital
Venue:
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Year:
2005

Citing 3
Cited 22

A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

Machine Learning
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

Artificial Intelligence in Medicine
Data mining techniques for cancer detection using serum proteomic profiling

Artificial Intelligence in Medicine

Clinical machine learning

Journal of Biomedical Informatics - Special issue: Clinical machine learning
Letters: Novelty detection with constructive probabilistic neural networks

Neurocomputing
Selection of human embryos for transfer by Bayesian classifiers

Computers in Biology and Medicine
Prediction of periventricular leukomalacia. Part I: Selection of hemodynamic features using logistic regression and decision tree algorithms

Artificial Intelligence in Medicine
Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes

Computers in Biology and Medicine
Adaptive knowledge discovery for decision support in intensive care units

WSEAS Transactions on Computers
A framework for detecting glaucomatous progression in the optic nerve head of an eye using proper orthogonal decomposition

IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Detecting change in dynamic fitness landscapes

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A novel feature selection approach for biomedical data classification

Journal of Biomedical Informatics
Independent component analysis applied to detection of early breast cancer signs

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Adaptive decision support for intensive care

EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Transcriptional networks characterize ventricular dysfunction after myocardial infarction: A proof-of-concept investigation

Journal of Biomedical Informatics
Towards improving inter-team coordination in the surgical process: a breakdown detection method

Proceedings of the 1st ACM International Health Informatics Symposium
Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

Expert Systems with Applications: An International Journal
Research Article: Predicting protein-protein interactions using graph invariants and a neural network

Computational Biology and Chemistry
Hardware aware algorithm performance and the low power continuous wavelet transform

Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Combining classifiers using their receiver operating characteristics and maximum likelihood estimation

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Effects of data grouping on calibration measures of classifiers

EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
An energy efficient model for monitoring and detecting atrial fibrillation in wearable computing

Proceedings of the 7th International Conference on Body Area Networks
A framework for constructing benchmark databases and protocols for retinopathy in medical image analysis

IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
ROC curves for regression

Pattern Recognition
Application of neural networks in detection of abnormal brain perfusion regions

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Receiver operating characteristic (ROC) curves are frequently used in biomedical informatics research to evaluate classification and prediction models for decision support, diagnosis, and prognosis. ROC analysis investigates the accuracy of a model's ability to separate positive from negative cases (such as predicting the presence or absence of disease), and the results are independent of the prevalence of positive cases in the study population. It is especially useful in evaluating predictive models or other tests that produce output values over a continuous range, since it captures the trade-off between sensitivity and specificity over that range. There are many ways to conduct an ROC analysis. The best approach depends on the experiment; an inappropriate approach can easily lead to incorrect conclusions. In this article, we review the basic concepts of ROC analysis, illustrate their use with sample calculations, make recommendations drawn from the literature, and list readily available software.