
## 3.1  The Problem Of Patient Risk Group Detection

Early detection of artherosclerotic coronary heart disease (CHD) is an important and difficult medical problem. CHD risk factors include artherosclerotic attributes, living habits, hemostatic factors, blood pressure, and metabolic factors (Goldman et al., 1996). Their screening is performed in general practice by data collection in three different stages.

A Collecting anamnestic information and physical examination results, including risk factors like age, positive family history, weight, height, cigarette smoking, alcohol consumption, blood pressure, and previous heart and vascular diseases.

B Collecting results of laboratory tests, including information about risk factors like lipid profile, glucose tolerance, and trombogenic factors.

C Collecting ECG at rest test results, including measurements of heart rate, left ventricular hypertrophy, ST segment depression, cardiac arrhythmias and conduction disturbances.

Our goal was to construct at least one relevant and interesting subgroup, called a pattern in the rest of the work, for each stage, A, B, and C, respectively.

A database with 238 patients representing typical medical practice in CHD diagnosis, collected at the Institute for Cardiovascular Prevention and Rehabilitation, Zagreb, Croatia, was used for subgroup discovery.  The database is in no respect a good epidemiological CHD database reflecting actual CHD occurrence in a general population, since about 50% of gathered patient records represent CHD patients. Nevertheless, the database is very valuable since it includes records of different types of the disease. Moreover, the included nnegative cases (patients who do not have CHD) are not randomly selected persons but individuals with some subjective problems or those considered by general practitioners as potential CHD patients, and hence sent for further investigations to the Institute. This biased data set is appropriate for CHD risk group discovery, but it is inappropriate for measuring the success of CHD risk detection and for subgroup performance estimation in gneral medical practice.

## 3.2 Results Of Expert-Guided Subgroup Detection And Selection

The process of expert-guided subgroup discovery was performed as follows. For every data stage A, B and C, the DMS algorithm was run for values g in the range 0.5 to 100, and a fixed number of selected output rules equal to 3. The rules induced in this iterative process were shown to the expert for selection and interpretation. The inspection of 15–20 rules for each data stage triggered further experiments. Concrete suggestions of the medical expert involved in this study were to limit the number of features in the rule body and to try to avoid the generation of rules whose features would involve expensive and/or unreliable laboratory tests. Consequently, we have performed the further experiments by intentionally limiting the feature space and the number of iterations in the main loop of the SD algorithm
(steps 2-12 of Algorithm SD).

In this iterative process, the expert has selected five interesting CHD risk groups. Table 1 shows the induced subgroups, together with the values of g and the rule significance. In the subgroup discovery terminology proposed in this paper, the features appearing in the