The importance of post-processing the results of clustering when using data mining to support subsequent decision-making is discussed. Both the formal embedded binary logistic regression (EBLR) and the visual profile’s assessment grid (PAG) methods are presented as bridging tools for the real use of clustering results. EBLR is a sequence of logistic regressions that helps to predict the class of a new object; while PAG is a graphical tool that visualises the results of an EBLR. PAG interactively determines the most suitable class for a new object and enables subsequent follow-ups. PAG makes the underlying mathematical model (EBLR) more understandable, improves usability and contributes to bridging the gap between modelling and decision-support. When applied to medical problems, these tools can perform as diagnostic-support tools, provided that the predefined set of profiles refer to different stages of a certain disease or different types of patients with a same medical problem, etc. Being a graphical tool, PAG enables doctors to quickly and friendly determine the profile of a patient in the everyday activity, without necessarily understanding the statistical models involved in the process, which used to be a serious limitation for wider application of these methods in clinical praxis. In this work, an application is presented with 4 functional disability profiles.

Peer Reviewed