Active subgroup mining: a case study in coronary heart disease risk group detection

Abstract 

This paper presents an approach to active mining of patient records aimed at discovering patient groups at high risk for coronary heart disease (CHD). The approach proposes active expert involvement in the following steps of the knowledge discovery process: data gathering, cleaning and transformation, subgroup discovery, statistical characterization of induced subgroups, their interpretation, and the evaluation of results. As in the discovery and characterization of risk subgroups, the main risk factors are made explicit, the proposed methodology has high potential for patient screening and early detection of patient groups at risk for CHD.

Keywords:  Coronary heart disease, Active mining, Machine learning, Subgroup discovery, Risk group detection, Non-invasive cardiovascular tests