Visualization of Decision Rules - from the Cardiologist's Point of View

A lot of decision systems work internally using different forms of decision rules. In our experiments on large medical datasets, we found that when the number of conditions in a decision rule increases and the overall number of rules is greater than 20-50, it is really difficult to analyze and manage the stored knowledge. Our research concentrated on two methods of the visualization of decision rules: decision trees (AQDT-2 algorithm) and the so-called rule-diagrams, which present conditional parts of decision rules in a 3D matrix (2D layers are stacked in 3D cube). Experts agreed that decision trees present an attractive possibility of data visualization for small sets of rules (up to 100). They are intuitively understandable and with our own extensions, they provide a quick method for checking several what-if scenarios. For larger sets of rules (more then 100) rule-diagrams are definitely the better methods for analyzing patterns within data.