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9th International Conference on Neural Information Processing, ICONIP 2002

作者： Zorman, M. Kokol, P. Laboratory for System Design Faculty of Electrical Engineering and Computer Science University of Maribor MariborSI-2000 Slovenia

ISBN: (纸本)9810475241

Transforming knowledge between connectionist and symbolic machine learning approaches is not a uniform task and there is no general recipe for performing it. Irrespective of the way the transformation of knowledge, the developed methods usually fall into two classes: methods with high rate of transformed knowledge is done, but with a lot of restrictions concerning the type of data in the training sets, training methods, and the size of data sets;and methods with moderate rate of transformed knowledge, but with less restrictions. Our main interest was to find or develop a technique that would possess the knowledge acquisition power of neural networks and explanation power of decision trees. That is why we developed a NN-DT Cascade method, an embedded hybrid of neural networks and decision trees, which is capable of transforming a part of knowledge from the neural network into a decision tree. © 2002 Nanyang Technological University.

关键词： Decision trees

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IFSIMS - Internet Framework Service for Intelligent Medical systems

IFSIMS - Internet Framework Service for Intelligent Medical ...

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Annual IEEE Symposium on computer-Based Medical systems

作者： M. Lenic P. Kokol R. Yamamoto Laboratory for system design Faculty of electrical engineering and computer science University of Maribor Slovenia

Modern medicine generates huge amounts of sensitive data. Data mining and knowledge discovery systems can be used to extract useful information/knowledge in order to to improve future diagnoses and reduce costs. On the other hand, when analysing data, the most recent methods, that extract the most accurate knowledge and decision models, should be used. This can be established with a service enabling quick access to newly developed methods. In this paper, a service for doing this, named IFSIMS (Internet Framework Service for Intelligent Medical systems), is presented.

关键词： Intelligent systems Data mining Medical diagnostic imaging Data analysis Web and internet services Laboratories electrical engineering computer science Biomedical informatics Educational institutions

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Mining diabetes database with decision trees and association rules

Mining diabetes database with decision trees and association...

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Annual IEEE Symposium on computer-Based Medical systems

作者： M. Zorman G. Masuda P. Kokol R. Yamamoto B. Stiglic Laboratory for system design Faculty of electrical engineering and computer science University of Maribor Slovenia Osaka Medical College Takatsuki Osaka Japan

Searching for new rules and new knowledge in problem areas, where very little or almost none previous knowledge is present, can be a very long and demanding process. In our research we addressed the problem of finding new knowledge in the form of rules in the diabetes database using a combination of decision trees and association rules. The first question we wanted to answer was, if there are significant differences in sets of rules both approaches produce, and how rules, produced by decision trees behave, after being a subject of filtering and reduction, normally used in association rule approaches. In order to accomplish that, we had to make some modifications to both the decision tree approach and association rule approach. From the first results we can conclude, that the sets of rules, built by decision trees are much smaller than the sets created by association rules. We could also establish, that filtering and reduction did not effect the rules derived from decision trees in the same scale as association rules.

关键词： Data mining Diabetes Databases Decision trees Association rules Filtering Educational institutions Medical diagnostic imaging electrical engineering computer science

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Supporting medical decisions with vector decision trees

Supporting medical decisions with vector decision trees

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10th World Congress on Medical Informatics, MEDINFO 2001

作者： Šprogar, Matej Kokol, Peter Zorman, Milan Podgorelec, Vili Yamamoto, Ryuichi Masuda, Gou Sakamoto, Norihiro Laboratory for System Design Faculty of Electrical Engineering and Computer Science University of Maribor Smetanova 17 SI-2000 Maribor Slovenia Osaka Medical College Takatsuki Japan Kyushu University Fukuoka Japan

ISBN: (纸本)1586031945

The article presents the extension of a common decision tree concept to a multidimensional - vector - decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample. It has the functionality of many separate decision trees acting on a same set of training data and answering different questions. Vector decision tree is therefore simple in its form, is easy to use and analyse and can express some relationships between decisions not visible before. To explore and test the possibilities of this concept we developed a software tool - DecRain - for building vector decision trees using the ideas of evolutionary computing. Generated vector decision trees showed good results in comparison to classical decision trees. The concept of vector decision trees can be safely and effectively used in any decision making process. © 2001 IMIA. All right reserved.

关键词： Machine learning

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Comparison of three databases with a decision tree approach in the medical field of acute appendicitis

Comparison of three databases with a decision tree approach ...

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10th World Congress on Medical Informatics, MEDINFO 2001

作者： Zorman, Milan Eich, Hans-Peter Kokol, Peter Ohmann, Christian Laboratory for System Design Faculty of Electrical Engineering and Computer Science University of Maribor Smeatnova 17 SI-2000 Maribor Slovenia Theoretical Surgery Unit Dept. of General and Trauma Surgery Heinrich-Heine University Düsseldorf Germany

ISBN: (纸本)1586031945

Decision trees have been successfully used for years in many medical decision making applications. Transparent representation of acquired knowledge and fast algorithms made decision trees one of the most often used symbolic machine learning approaches. This paper concentrates on the problem of separating acute appendicitis, which is a special problem of acute abdominal pain from other diseases that cause acute abdominal pain by use of an decision tree approach. Early and accurate diagnosing of acute appendicitis is still a difficult and challenging problem in everyday clinical routine. An important factor in the error rate is poor discrimination between acute appendicitis and other diseases that cause acute abdominal pain. This error rate is still high, despite considerable improvements in history-taking and clinical examination, computer-aided decision-support and special investigation, such as ultrasound. We investigated three different large databases with cases of acute abdominal pain to complete this task as successful as possible. The results show that the size of the database does not necessary directly influence the success of the decision tree built on it. Surprisingly we got the best results from the decision trees built on the smallest and the biggest database, where the database with medium size (relative to the other two) was not so successful. Despite that we were able to produce decision tree classifiers that were capable of producing correct decisions on test data sets with accuracy up to 84%, sensitivity to acute appendicitis up to 90%, and specificity up to 80% on the same test set. © 2001 IMIA. All right reserved.

关键词： Decision trees

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Evolving groups of basic decision trees

Evolving groups of basic decision trees

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Annual IEEE Symposium on computer-Based Medical systems

作者： M. Sprogar P. Kokol M. Zorman V. Podgorelec L. Lhotska J. Klema Faculty of Electrical Engineering and Computer Science Laboratory for System Design Maribor Slovenia Gerstner Laboratory Faculty of Electrical Enginéering Czech Technical University Prague Czech Republic

A decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification into such subsets is not possible, it is better to put the classification decision on to some other classifier. This leads to the introduction of a null classification, which simply means that no classification is possible in this step. This approach is sensible with evolutionary methods, as they can handle a number of trees simultaneously. In the process of construction, we have to address the problem of whether a classification is sensible. The performance of the proposed model has been tested on several data sets and the results presented on one such data set show its potential.

关键词： Decision trees Testing Classification tree analysis Medical diagnostic imaging Decision making Laboratories computer science Humans Intelligent systems Genetic algorithms

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The influence of class discretization to attribute hierarchy of decision trees

The influence of class discretization to attribute hierarchy...

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15th Congress on Medical Informatics Europe, MIE 1999

作者： Babič, Špela Hleb Kokol, Peter Zorman, Milan Podgorelec, Vili Faculty of Electrical Engineering and Computer Science University of Maribor Laboratory for System Design Smetanova 17 2000 Maribor Slovenia

ISBN: (纸本)0967335515

Decision support systems that help physicians are becoming very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable and quick response. One of the most viable among decision-making models is the concept of decision trees, already successfully used for many medical decision making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision supporting models, each of them built with different discretization of attributes and decision classes. For the construction of decision trees we used MtDeciT, in our laboratory developed tool for building decision trees using the classical induction method. All solutions were evolved for determining the influence of basic properties of child and his/her parents to length of successful breastfeeding. A comparison between developed models and obtained results has shown that the way of discretization obviously plays a great role in the reliable and accurate real-world medical decision making.

关键词： Decision trees

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CArE: A software package for computer-aided nurse education

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Health Informatics Journal 1999年第3期5卷 119-123页

作者： Habjanǐ, A. Kokol, P. Zorman, M. Šlajmer-Japelj, M. The University College of Nursing Studies Maribor Zitna Ulica 15 2000 Slovenia Faculty of Electrical Engineering and Computer Science Laboratory for System Design Maribor Smetanova 17 2000 Slovenia

computer-managed instruction (CMI) has been used in nursing education since the late 1960s. It is because of its accessibility and self-paced format that CMI is very well suited both to students and practising nurses, as learning can take place at the learner's own pace and in his or her own time. In addition CMI also supports continuing education and distant learning. The aim of this paper is to present CArE - a software package for computer Aided Nurse Education and in particular, for teaching the basics of the nursing care process. This package has been developed as a result of the Phare TEMPUS project called NICE (Nursing Informatics and computer Aided Education) [14] [15].

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Analysis of superimposed signals using time-scale phase representation

Analysis of superimposed signals using time-scale phase repr...

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IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis

作者： A. Sostaric D. Zazula System Software Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia

In this paper we address the decomposition problem of electromyographic signals (EMG) and we present a new idea for the decomposition approach introducing the time-scale phase representation. Considering the nature of EMG signals, a special set of nonorthogonal conjugated basis functions is used to perform the time-scale analysis. Mapping the modified signal phase into the time-scale phase plane is applied. Its minimization leads towards a very good time localization of individual superimposed signal components.

关键词： Signal analysis Electromyography Muscles Shape Wavelet analysis Frequency domain analysis system software Laboratories computer science Performance analysis

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Royal Academy of Medicine in Ireland - Section of Bioengineering - Proceedings of meeting held in Dundalk, Co. Louth, in February 1998.

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IRISH JOURNAL OF MEDICAL science 1998年第4期167卷 256-276页

作者： [Anonymous] Orthopaedic Research Unit The Queen’s University of Belfast School of Biomedical Sciences The Queen’s University of Belfast Howmedica International Ltd. Raheen Industrial Estate Limerick Department of Mechanical Engineering Trinity College Dublin 2 Mater Hospital Dublin 7 Orthopaedic Research Laboratory Århus University Hospital Denmark University of Limerick Limerick Limerick Regional Hospital Raheen Limerick Boston Scientific Ireland Ltd. Galway Department of Mechanical Engineering University College Dublin Dublin Department of Electrical and Electronic Engineering University College Dublin Dublin Bioengineering Research Centre Department of Mechanical Engineering University College Dublin Dublin Northern Ireland Bio-Engineering Centre (NIBEC) School of Electrical and Mechanical Engineering University of Ulster Newtownabbey Department of Mechanical & Manufacturing Engineering The Ashby Building The Queen’s University of Belfast Johnson and Johnson Professional Orthopaedic Division Queensway England Department of Cardiology and Department of Medical Physics & Bioengineering St. James’s Hospital Dublin 8 Burke House Ellison Street Castlebar Royal College of Surgeons in Ireland Department of Surgery Beaumont Hospital Dublin Department of Electronic and Electrical Engineering Trinity College Dublin 2 Department of Aeronautical Engineering The Queen’s University of Belfast Vascular Unit Belfast City Hospital Department of Medical Physics and Bioengineering St. James’s Hospital Dublin 8 Centre for Biomedical Electronics Department of Electronic and Computer Engineering University of Limerick Limerick Department of Mechanical and Aeronautical Engineering University of Limerick Limerick School of Health Sciences University of Ulster Ireland School of Physiotherapy University of Ulster Newtownabbey Clinical Engineering NI Medical Physics Agency Belfast and Radiology Royal Victoria Hospital Musgrave Park Hospital University of Limerick Biomedical Institute (ULBMI) Uni

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