CHD patients and healthy subjects regarding their age, but that there are significant differences among the detected patterns. Figure 8 illustrates a similar effect for the total cholesterol values although it is known that total cholesterol is an important risk factor for the CHD disease. This observation shows that the problem of CHD risk group detection can typically not be solved by considering single features and demonstrates the appropriateness of the suggested approach which tries to generate subgroup descriptions which are a logical conjunction of a few correlated features.

Figure 10 is also interesting, since it is very different from other figures. Notice that exercise ECG ST segment depression was not used as an attribute in the training data
(which contained only attributes that are available at stages A, B and C); exercise ECG ST segment depression, long term ECG recording and echochardiography are not available for early risk group detection since they can be collected/measured only in specialized medical institutions. Figure 10 clearly demonstrates significant differences between all CHD and all healthy subjects in terms of exercise ECG ST segment depression values, demonstrating that this measurement, if available, is an excellent disease indicator. But it also shows that, although it is known that patterns A1 and C1 cover different disease subpopulations, they behave very similarly in terms of the exercise ECG ST segment depression property.

## 3.6  Subgroup Evaluation

In order to evaluate the discovered risk groups, the medical expert has tested the induced subgroup patterns on an independent set of 70 people (50 CHD patients and 20 non-CHD
cases from the same hospital). The results for these patients, summarized in Table 4, show that the patterns are successful in detecting CHD patients. About 90% of CHD patients were included into at least one of the five patterns. The detected sensitivity values ( TPr ) for patterns A1, B2, and C1 are significantly higher than the values computed on the set of patients used for subgroup discovery. For the other two patterns the values do not differ significantly. Note that the accuracy values are relatively high, despite the relatively high false positive rate ( FPr ): a lower FPr could have been achieved by selecting lower values of the generalization parameter g, at a cost of detecting subgroups with lower coverage of positive cases.

| Fraining set   | Test set   |       |       |       |       |       |
|----------------|------------|-------|-------|-------|-------|-------|
| TPr            | FPr        | T Pr  | FPr   |       |       |       |
| Accuracy       | Accuracy   |       |       |       |       |       |
| A 1            | 47.5%      | 26.8% | 59.4% | 84.8% | 7.8%  | 80.0% |
| A 2            | 48.4%      | 6.7%  | 81.2% | 41.2% | 27.3% | 70.0% |
| B1             | 28.8%      | 9.4%  | 72.7% | 36.0% | 20.0% | 81.8% |
| B2             | 69.2%      | 42.0% | 15.0% |       |       |       |
| 32.4%          | 12.6%      | 87.5% |       |       |       |       |
| C1             | 23.4%      | 5.5%  | 78.8% | 82.0% | 40.0% | 83.7% |

Table 4: Summary of results obtained on the training set and on an independent set of 70 persons (50 CHD patients and 20 non-CHD cases from the same hospital),
measured in terms of TPr, FPr and Accuracy.