1. Discovering Constrained Association Rules to Predict Heart Disease
Carlos Ordonez*, Edward Omiecinski, Levien de Braal, Cesar Santana, et.al.
Georgia Tech Emory University
*working for Teradata (NCR)
IEEE ICDM 2001

2. Motivation Goal: help in heart disease diagnosis
Basic Data Mining technique
Similar to expert system rules
Combinatorial: causes=>disease
Simplicity: easy to interpret
Privacy preserving
Reliability: having two statistical measures

3. Medical data issues
Rich attribute types. Attributes must be transformed into binary.
Small data set size, n=655 patients
Noisy, there exist many missing values. Errors in data collection.
Naive approach: thousands/millions of associations and rules. Negation makes problem worse

4. Good rules:
IF Age>=70, Smokes=Y, Gender=M THEN RCA>=50 s=0.4 c=1
IF Gender=F, Age<70 THEN LAD>=70 s=0.2 c=1.0
IF Gender=M, Age<70 THEN RCA<50
Bad rules:
IF Age>=70 THEN Smokes=Y
IF LAD>=70 THEN RCA>=50,
IF Gender=M,Age>=60,Smokes=Y THEN LAD,RCA

5. Algorithm overview Map attributes to items
Mine association rules (A-priori)
Phase 1: generate frequent associations above minimum
support
Phase 2: generate rules with minimum confidence

6. Mapping attributes to binary data
Uniformly treat as categorical or numerical
Manual: ranges are determined by MD
Each categorical value becomes an item
Each numerical range becomes an item.
Missing info handling simplified
Each value/range can be negated

7. Important constraints
Max rule size: simplicity. Phase 1 faster.
A: Antecedent, C: Consequent. Medically meaningful. Phase 2 faster.
G: Group constraint: eliminate trivial or irrelevant associations. Phase 1 and 2 faster.
Negation: more combinations
Support= 2/n

8. Medical attributes

9. Experimental results Minimum support frequency: 2 Max rule size: 4
Time: 12 minutes
Associations: 36, % of time
Rules: 2, % of time.

10. Medical significance Specificity Sensitivity
Gold standard: catheterization

11. Usage of rules
Confirming knowledge. Used to validate Expert System IF-THEN rules
Discovering knowledge. Surprising to domain expert.
Distinguish healthy and sick patients

12. Rules predicting no heart disease
IF Sex=F THEN 0<=LCX<50, s=22% c=73%
IF Smokes=N THEN not(70<=RCA<100.1), s=29% c=71%
IF Age<40,Diab=N THEN 0<=0 LAD<50, s=2% c=82%
IF 40<=Age<60,Sex=F,Diab=N THEN RCA<50, s=7% c=80%

13. Rules predicting heart disease
IF 0.2<=AP<1.1,PCarSur=Y THEN not(LAD<50) not(RCA<50), s=1% c=80%
IF 60<Age, 0.2<=AP<1.1,Smokes=Y THEN not(LAD<50) s=10% c=83%
IF 60<Age, 0.2<=SA<1.1,FHCAD=Y THEN not(LAD<50) s=2% c=100%
IF 60<Age, 0.2<=AP<1.1,Sex=F THEN not(LAD<50) s=5% c=94%

14. Conclusions Mapping attributes is required Constraining is essential
Some of the findings were unexpected
Future work: find more useful constraints, finer ranges, improve missing info handling, validate by clustering and decision trees