Supervised classification methods are applied in many fields. The main problem of applying these methods is how to select the most appropriate method in particular case. The literary review was fulfilled and the advantages and disadvantages of mostly used criterion of supervised classification methods comparisons were ascertained. Then the methodology of comparisons was suggested. The analysis of SAS system procedures and macro commands was made. It was ascertained that there is not comfortable software which allows comparing the results of supervised classification methods. This work demands a lot of work, good knowledge of SAS programming language and high qualification in programming. So, the main purpose of this work is to expand the statistical data analysis system SAS possibilities in comparison of supervised classification methods and classificate various data. In this work the possibilities of SAS system are expanded by the tool which allows comparing quality of the linear, quadratic, kernel, nearest neighbor’s discriminant analysis and logistic regression analysis methods. There were used classification error estimates which were got by resubstition, cross–validation leave one out, bootstrap and Monte Carl cross–validation methods, although classification error confidence intervals which were got by non-parametric bootstrap method. The test of created tool was made with various data (different sample sizes, various classis separability, violations of assumptions... [to full text]

Coronary arteries stenosis causes ischemic heart disease which is the main fatality reason all over the world. For diagnosis arteries stenosis invasive and noninvasive methods are used. These methods are quite expensive and not all medical institutions can carry out such tests. Analysis of electrocardiogram could be one of the cheapest and current methods to diagnose arteries stenosis. Despite the fact, that in some cases such analysis is not very informative, medics look for the informative ECG parameters and their combinations in order to predict stenosis. The goal of this work is to create statistical methods and software for prognosis of coronary arteries stenosis using digital ECG parameters. Data was colecet and prepared for this reseach in Clinic of Cardiology of Kaunas Medical University. In this work are presented: 1.Statistical analysis models for prognosis of coronary arteries stenosis. 2.Software for uses. 3.Comparative analysis of statistical analysis models. 4.Results of real data analysis, which were obtained by using, developed statistical models and software. Obtained results will be used to improve methods of diagnosis ischemic heart disease and arteries stenosis in Clinic of Cardiology of Kaunas Medical University.