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A classifier based on a decision tree with verifying cuts
(Humboldt University, 2014) Bazan, Jan G.; Bazan-Socha, Stanisława; Buregwa-Czuma, Sylwia; Dydo, Łukasz; Rząsa, Wojciech; Skowron, Andrzej
This article introduces a new method of a decision tree construction. Such decision tree is constructed with the usage of additional cuts that are used for a veri cation of cuts in tree nodes during the classi cation of objects. The presented approach allows the use of additional knowledge contained in the attributes which could be eliminated using greedy methods. The paper includes the results of experiments that have been performed on data obtained from biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its outcomes with the results of classi cation using a local discretization decision tree, well known from literature. The results of comparison of the two approaches show that making decisions is more adequate through the employment of several attributes simultaneously. Our new method allowed us to achieve better quality of classi cation then the existing method.
Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring.
(IEEE Xplore, 2012) Bazan-Socha, Stanisława; Bazan, Jan G.; Buregwa-Czuma, Sylwia; Pardel, Przemysław W.; Sokolowska, Barbara
The purpose of this study was to evaluate the usefulness of classification methods in recognizing cardiovascular pathology. Based on clinical and electrocardiographic (ECG) Holter data we propose the method for predicting coronary stenosis demanding revascularization in patients with diagnosis of stable coronary heart disease. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzisław Pawlak during the early 1980s provides the foundation for the construction of classifiers. From the rough set perspective, classifiers presented in the paper are based on a decision tree calculated on the basis of the local discretization method. We present a new modification of tree building method which emphasizes the discernibility of objects belonging to decision classes indicated by human experts. Presented method may be used to assess the need for revascularization and in special circumstances, to confirm or reject the diagnosis of coronary artery disease. The paper includes results of experiments that have been performed on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Krakow, Poland.
Prediction of coronary arteriosclerosis in stable coronary heart disease
(Springer-Verlag, 2012) Bazan, Jan G.; Bazan-Socha, Stanisława; Buregwa-Czuma, Sylwia; Pardel, Przemysław W.; Sokolowska, Barbara
The aim of the study was to assess the usefulness of classification methods in recognizing cardiovascular pathology. From the medical point of view the study involves prediction of coronary arteriosclerosis presence in patient with stable angina using clinical data and electrocardiogram (ECG) Holter monitoring records. On the grounds of these findings the need for coronary interventions is determined. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzislaw Pawlak during the early 1980s provides the foundation for the construction of classifiers. From the rough set perspective, classifiers presented in the paper are based on a decision tree calculated on the basis of the local discretization method. The paper includes results of experiments that have been performed on medical data obtained from II Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland.

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