Assessment of cardiovascular disease risk prediction models: evaluation methods

Authors:
Richi Nayak;Ellen Pitt
Affiliations:
Computer Science Discipline, Queensland University of Technology, Brisbane, QLD, Australia;Computer Science Discipline, Queensland University of Technology, Brisbane, QLD, Australia
Venue:
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Year:
2011

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Abstract

This paper uses a real world anaesthesia time-series monitoring data in the prediction of cardiovascular disease risk in a manner similar to exercise electrocardiography. Models derived using the entire anaesthesia population and subgroups based on pre-anaesthesia likelihood of complications are compared in an attempt to ascertain which model performance measures are best suited to populations with differing pre-test probability of disease. Misclassification rate (MR) as a measure of prediction model performance was compared with Kappa statistic, sensitivity, specificity, positive and negative predictive values and area under the receiver operating characteristic curve (AUC). In this medical application of data mining, MR is shown to be dependent on the prevalence of disease within the population studied but AUC and Kappa statistic are shown to be independent of disease prevalence.