Coronary Risk Prediction by Logical Analysis of Data

Sorin Alexe (), Eugene Blackstone, Peter Hammer (), Hemant Ishwaran, Michael Lauer () and Claire Pothier Snader

Annals of Operations Research, 2003, vol. 119, issue 1, 15-42

Abstract: The objective of this study was to distinguish within a population of patients with known or suspected coronary artery disease groups at high and at low mortality rates. The study was based on Cleveland Clinic Foundation's dataset of 9454 patients, of whom 312 died during an observation period of 9 years. The Logical Analysis of Data method was adapted to handle the disproportioned size of the two groups of patients, and the inseparable character of this dataset – characteristic to many medical problems. As a result of the study, we have identified a high-risk group of patients representing 1/5 of the population, with a mortality rate 4 times higher than the average, and including 3/4 of the patients who died. The low-risk group identified in the study, representing approximately 4/5 of the population, had a mortality rate 3 times lower than the average. A Prognostic Index derived from the LAD model is shown to have a 83.95% correlation with the mortality rate of patients. The classification given by the Prognostic Index was also shown to agree in 3 out of 4 cases with that of the Cox Score, widely used by cardiologists, and to outperform it slightly, but consistently. An example of a highly reliable risk stratification system using both indicators is provided. Copyright Kluwer Academic Publishers 2003

Keywords: classification; data mining; Logical Analysis of Data; partially defined Boolean functions; risk indices; risk prediction (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1023/A:1022970120229

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