Rough Set Theory and Decision Rules in Data Analysis of Breast Cancer Patients

Abstract

Abstract.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.

Web Based Health Recommender System Using Rough Sets, Survival Analysis and Rule-Based Expert Systems

“…4) to evaluate the potential of rough sets, artificial neural networks and the frailty index in predicting survival time [6]. As reported, the prediction performance of rules induced by rough sets approaches were comparable with results of using other learning systems [5,6,11]. Survival analysis (see our previous studies [7,8,9,10,11]) attempts to answer questions such as: "Is diabetes (or others) a significant risk factor for geriatric patients?"…”

“…They illustrated that rough sets can contribute to a medical expert system. Zaluski et al applied rough sets to construct decision rules that classify a binary target function: cancer recurrence [5]. The authors provided a comparison of several approaches compared to rough sets.…”

Neighborhood Upper and Lower Approximation for the Content-Based Image Retrieval

“…In recent years we witnessed a rapid growth of interest in rough set theory and its applications, worldwide. Researchers focused their attention on reduction and classification algorithms based on rough set [5][6][7].…”

Applying rough sets to prevent customer complaints for IC packaging foundry

“…The RST was introduced by Pawlak (1982Pawlak ( , 1991 as a useful tool to deal with data with uncertainty, reduce the size of data sets, find hidden patterns, and generate decision rules. Some recent applications on RST are medical cases (Ilczuk & Wakulicz-Deja, 2005;Wilk, 2005;Zaluski, Szoszkiewicz, Krysiń ski, & Stefanowski, 2004), business problems (Dimitras, Slowinski, Susmaga, & Zopounidis, 1999;Kumar & Agrawal, 2005;Shen & Loh, 2004), semiconductor manufacturing (Kusiak, 2001a(Kusiak, , 2001b. Komorowski, Pawlak, Polkowski, and Skowron (1999) provided a comprehensive tutorial and applications on rough sets; Pawlak (2004) proposed some recent research directions of rough sets.…”