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Selection of classifier in acute abdominal pain diagnosis with decision tree model

Burduk R.

Journal of Medical Informatics & Technologies

2011

Vol. 17

65--71

The article presents the application of the decision tree classifier to the acute abdominal pain diagnosis. The recognition task model is based on a decision tree. In this model the decision tree structure is given by the experts. For the assumed structure of the decision tree the locally optimal strategy is considered. The problem discussed in the work shows a selection of different classifiers (parameters) to the internal nodes of the decision tree. Experiments conducted for selected medical diagnosis problem shows that the use of different parameters for k-NN classification can improve the quality of classification in comparison with the situation if it is used with the same parameter for all internal nodes of the decision tree.

Pattern recognition of sacroileitis with the use of multistage logic with a fuzzy loss function

Burduk R.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2004

Vol. 8, No 2

217-221

The article describes the problem of pattern recognition of sacroileitis. Classification is based on a scheme of multistage recognition with a fuzzy loss function dependent on the node of the decision tree. Decision rules are based on k-nearest neighbors at particular internal nodes of the decision-tree. Paper presents influence of comparison fuzzy numbers on classifications results.

Distance based classifiers and their use to analysis of data concerned acute coronary syndromes

Jóźwik A., Stawska Z., Grabowski M., Filipiak K., Rudowski R., Opolski G.

Image Processing & Communications

2003

Vol. 9, no 3-4

45-52

The paper presents effectiveness of classifiers based on distance function in application to real problem concerned acute coronary syndromes. The types of decision rules: the standard k-NN rule; its fuzzy version and the multistage decision rule that uses the class overlap idea are considered. In the case of the fuzzy k-NN rule the fuzzyness is applied only for decreasing a misclassification rate. The multistage classifier is taken into account because of its very desired property, which consist in possibility of determination whether a case being classified is difficult or easy for recognition. The more difficult is the case to be classified the more stages are required. This property enables an error rate gradation. In each stage the proposed classifier can make up one of the three following decisions: indicate a class number, reply :"I do not know" or qualifythe object to the next stage. A number of stages depend on the classified object. The analyzed data concern to the two-class decision problem that consist in prediction whether the patient will survive the period of one month or not.