# Expert-Guided Subgroup Discovery: Methodology And Application

DRAGAN. GAMBERGER@IRB. HR
Dragan Gamberger Rudjer Bošković Institute, Bijenička 54 1000 Zagreb, Croatia

## Nada Lavrač

Jožef Stefan Institute, Jamova 39 1000 Ljubljana, Slovenia NA DA. LAV RA C @ I J S. SI

## Abstract

This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The other important steps of the proposed subgroup discovery process are the detection of statistically significant properties of selected subgroups and subgroup visualization: statistically significant properties are used to enrich the descriptions of induced subgroups, while the visualization shows subgroup properties in the form of distributions of the numbers of examples in the subgroups. The approach is illustrated by the results obtained for a medical problem of early detection of patient risk groups.

## 1. Introduction

This paper addresses the problem of subgroup discovery which can be defined as: given a population of individuals and a property of those individuals we are interested in, find population subgroups that are statistically 'most interesting', e.g., are as large as possible and have the most unusual statistical (distributional) characteristics with respect to the property of interest (Klösgen, 1996; Wrobel, 1997, 2001). Its main contribution is a new methodology supporting the process of expert-guided subgroup discovery. Specifically, we introduce a novel parametrized definition of rule quality used in a heuristic beam search algorithm, a rule subset selection algorithm incorporating example weights, the detection of statistically significant properties of selected subgroups, and a novel subgroup visualization method. An in-depth analysis of the proposed quality measure is provided as well. The proposed methodology has been applied to the medical problem of detecting and describing patient groups with high risk for artherosclerotic coronary heart disease (CHD). 1 The paper organization is as follows. Algorithms for subgroup detection and selection, which are the main ingredients of the expert-guided subgroup discovery methodology, are described in Section 2. Section 3 presents: the coronary heart disease risk group detection problem, the discovered patient risk groups, their statistical aracterization, visualization, medical interpretation and evaluation, including a discussion on the expert's role in