Rough set theory and decision rules in data analysis of breast cancer patients

In this paper an approach based on the rough set theory and induction of decision rules is applied to analyse relationships between condition attributes describing breast cancer patients and their treatment results. The data set contains 228 breast cancer patients described by 16 attributes and is divided into two classes: the 1st class - patients who had not experienced cancer recurrence, the 2nd class - patients who had cancer recurrence. In the first phase of the analysis, the rough sets based approach is applied to determine attribute importance for the patients' classification. The set of selected attributes, which ensured high quality of the classification, was obtained. Then, the decision rules were generated by means of the algorithm inducting the minimal cover of the learning examples. The usefulness of these rules for predicting therapy results was evaluated by means of the cross-validation technique. Moreover, the syntax of selected rules was interpreted by physicians. Proceeding in this way, they formulated some indications, which may be helpful in making decisions referring to the treatment of breast cancer patients. To sum up, this paper presents a case study of applying rough sets theory to analyse medical data.