Prediction of Coronary Arteriosclerosis in Stable Coronary Heart Disease

Bazan, Jan G.; Bazan-Socha, Stanisława; Buregwa-Czuma, Sylwia; Pardel, Przemyslaw Wiktor; Sokołowska, Barbara

doi:10.1007/978-3-642-31715-6_58

Cited by 8 publications

(1 citation statement)

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“…The data analysis employing the above method of a cut quality measurement performed for clinical and laboratory data together with 24-hour Holter ECG monitoring in limited group of patients, have led to the development of preliminary methods with 94% of sensitivity and 80% of specificity in predicting the necessity of revascularization (see [5]). The results are much better then those with 78% of sensitivity and 73% of specificity received using the same method of classifier building, but without implementation of the domain knowledge (see [6]). …”

Section: Improving the Classical Methods Of Classifier Constructionmentioning

confidence: 88%

A Domain Knowledge as a Tool For Improving Classifiers

Bazan

Buregwa-Czuma

Jankowski

2013

Fundamenta Informaticae

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This paper investigates the approaches to an improvement of classifiers quality through the application of a domain knowledge. The expertise may be utilizable on several levels of decision algorithms such as: feature extraction, feature selection, a definition of temporal patterns used in an approximation of the concepts, especially of the complex spatio-temporal ones, an assignment of an object to the concept and a measurement of the objects similarity. The domain knowledge incorporation results then in the reduction of the size of searched spaces. The work constitutes an overview of classifier building methods efficiently utilizing the expertise, worked out latterly by Professor Andrzej Skowron research group. The methods using domain knowledge intended to enhance the quality of classic classifiers, to identify the behavioral patterns and for automatic planning are discussed. Finally it answers a question whether the methods satisfy the hopes vested in them and indicates the directions for future development.

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Section: Improving the Classical Methods Of Classifier Constructionmentioning

confidence: 88%