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.
| Original language | English |
|---|---|
| Pages (from-to) | 15-42 |
| Number of pages | 28 |
| Journal | Annals of Operations Research |
| Volume | 119 |
| Issue number | 1-4 |
| DOIs | |
| State | Published - Mar 1 2003 |
| Externally published | Yes |
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Keywords
- Classification
- Data mining
- Logical Analysis of Data
- Partially defined Boolean functions
- Risk indices
- Risk prediction
ASJC Scopus subject areas
- Management Science and Operations Research
- Decision Sciences(all)
Cite this
Coronary Risk Prediction by Logical Analysis of Data. / Alexe, Sorin; Blackstone, Eugene; Hammer, Peter L.; Ishwaran, Hemant; Lauer, Michael S.; Pothier Snader, Claire E.
In: Annals of Operations Research, Vol. 119, No. 1-4, 01.03.2003, p. 15-42.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Coronary Risk Prediction by Logical Analysis of Data
AU - Alexe, Sorin
AU - Blackstone, Eugene
AU - Hammer, Peter L.
AU - Ishwaran, Hemant
AU - Lauer, Michael S.
AU - Pothier Snader, Claire E.
PY - 2003/3/1
Y1 - 2003/3/1
N2 - 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.
AB - 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.
KW - Classification
KW - Data mining
KW - Logical Analysis of Data
KW - Partially defined Boolean functions
KW - Risk indices
KW - Risk prediction
UR - http://www.scopus.com/inward/record.url?scp=0037245469&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0037245469&partnerID=8YFLogxK
U2 - 10.1023/A:1022970120229
DO - 10.1023/A:1022970120229
M3 - Article
AN - SCOPUS:0037245469
VL - 119
SP - 15
EP - 42
JO - Annals of Operations Research
JF - Annals of Operations Research
SN - 0254-5330
IS - 1-4
ER -