Wyniki wyszukiwania - BazTech

1

Application of ROC analysis to bayesian classifiers of PERG signals

Włodarski M.

Prace Naukowe Politechniki Śląskiej. Elektryka

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2011

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z. 1

111-120

EN

This paper presents the use of ROC analysis for the assessment of the classifiers’ performance. Either linear or quadratic discriminant analysis assigns objects to classes on the basis of parametric model. Fitting decision boundary according to nonparametric ROC curve allows to achieve demanded criteria like maximum accuracy, minimum risk or Neyman-Pearson’s criterion. This method was applied to measure the quality of bayesian classifiers of PERG signal real data base.

PL

Artykuł przedstawia wykorzystanie analizy ROC do oceny działania klasyfikatorów. Zarówno liniowa, jak i kwadratowa analiza dyskryminacyjna przyporządkowuje obiekty do klas na podstawie modelu parametrycznego. Dopasowanie granicy decyzyjnej zgodnie z nieparametryczną krzywą ROC pozwala osiągnąć pożądane kryterium: maksymalną skuteczność, minimalne ryzyko lub kryterium Neymana-Pearsona. Metodę tę zastosowano do oceny jakości klasyfikatorów bayesowskich rzeczywistej bazy danych sygnałów PERG.

2

Estimation of significance of AlkB and AlkA proteins in DNA repair in Escherichia coli model

Sokołowska B., Maciejewska A. M., Jóźwik A., Kuśmierek J. T.

Journal of Medical Informatics & Technologies

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2011

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Vol. 17

321--326

EN

The paper concerns estimation of significance of differences of mutagenesis level between the wild-type strain (wt) and its derivatives which differ in DNA repair ability, namely alkA and alkB strain, devoided AlkA glycosylase and AlkB dioxygenase activity, respectively. The strains were analyzed for their ability to repair 1,N6-ethenoadenine (εA) - chloroacetaldehyde adduct to DNA. The analysis was done using classical statistical and pattern recognition methods. The obtained results confirmed that AlkB dioxygenase plays the most important role in εA repair in E. coli in the experimental modeling.

3

Evaluation of predictive capabilities of quantitative cardiotocographic signal features

Czabański R., Jeżewski M., Roj D., Szaszkowski Z., Kupka T., Wróbel J.

Journal of Medical Informatics & Technologies

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2010

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Vol. 16

11--17

EN

Cardiotocography (CTG) is the main method of assessment of the fetal state during pregnancy and labour used in clinical practice. It is based on quantitative analysis of fetal heart rate, fetal movements and uterine contractions signals. The evaluation of the CTG signals can be made using criteria recommended by International Federation of Obstetrics and Gynecology. Nevertheless, the diagnosis verification is possible only after the delivery on the basis of newborn assessment. In the proposed work we evaluated the capacity of quantitative analysis of CTG traces in predicting fetal outcome. The relationship between CTG signal features and attributes of fetal outcome was assessed on the basis of ROC curves analysis. The obtained results indicate the adequate predictive capabilities of the selected CTG features especially for fetal outcome assessed with Apgar score and suggest the necessity of applying the criteria for the CTG traces evaluation that are related to the gestational age. Our study also shows the value of the CTG monitoring as a screening procedure providing appropriate confirmation of fetal wellbeing.

4

Porównanie parametrycznej i nieparametrycznej metody obliczania krzywej ROC na przykładzie zbioru sygnałów elektroretinograficznych

Włodarski M.

Metody Informatyki Stosowanej

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2009

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nr 2 (19)

177-192

EN

In this paper an introduction to two-class ROC analysis with its application for medical decision making is presented. ROC graph is a simple method to show classifier 's performance by visualization of the trade-off between specificity and sensitivity, but also the area under ROC curve (AUC) is a measure of the test 's ability to discriminate between two altemative states. In this article two approaches (parametric and non-parametric) for ROC curve generation are compared on patern electroretinogram (PERG) data set. The ROC analysis can be adopted in the machine learning as the technique for comparing and organizing decision systems.