Grzegorz Ilczuk

In the research presented here, a joint team (cardiologists and software specialists) focused on the evaluation of previously created, multi-stage decision systems trained on real clinical data. The question is how a decision system trained on a large database recognizes new cases remains unanswered? Obtained results seem to confirm that decision s...

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...

Implantation of a cardiac pacemaker is a complicated procedure. The success of the procedure depends directly on the proper classification of patients and the choice of the type of pacing. Machine learning algorithms can support this process. The most important element of these is the feature selection process. In this paper we present the results...

An ability of Pawlak’s Rough Sets Theory to handle imprecision and uncertainty without any need of preliminary or additional information about analyzed data makes this theory very interesting for analyzing medical data. Using Rough Sets Theory knowledge extracted from raw data may be stored in form of decision rules. But increasing number and compl...

Data preparation is a very important but also a time consuming part of a Data Mining process. In this paper we describe a hierarchical method of text classification based on regular expressions. We use the presented method in our data mining system during a pre-processing stage to transform Latin free-text medical reports into a decision table. Suc...

Success of machine learning algorithms is usually dependent on a quality of a dataset they operate on. For datasets containing noisy, inadequate or irrelevant information these algorithms may produce less accurate results. Therefore a common pre-processing step in data mining domain is a selection of highly predictive attributes. In this case study...

There has been huge progress in the introduction of new digital methods, such as decision support, in cardiology. Data preparation is the most important and the most time-consuming part of the data mining process. We present a newly developed hierarchical method of text classification based on regular expressions. This method is the basis of our da...

The process of discovering natural phenomena or complex system was until recently limited to finding formulas that fit empirical data. This process used with success in science and engineering has its limits when the complexity of the natural processes increases. Therefore, to analyze data in the medical domain an alternative approach was needed. S...

Success of many learning schemes is based on selection of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the process model can result in poor predictive accuracy and increased computation. This paper shows that the accuracy of classification can be improved by selecting subsets of strong...

Applying international guidelines in medical, including cardiological, therapies is a guarantee of safe and modern treatment. Unfortunately, standards are often not obeyed. In this paper we present an experimental software program based on rough sets methods. The main aim of this application is to improve patient care and help the decision process...

Pawlak’s Rough Sets Theory is one of many mathematical approaches to handle imprecision and uncertainty. The main advantage of the theory over other techniques is that it does not need any preliminary or additional information about analyzed data. This feature of rough set theory favors its usage in decision systems where new relations among data m...