An analysis of a lymphoma/leukaemia dataset using rough sets and neural networks

Authors:
Kenneth Revett;Marcin Szczuka
Affiliations:
University of Westminster, Harrow School of Computer Science, London, UK;Institute of Mathematics, Warsaw University, Warsaw, Poland
Venue:
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
Year:
2006

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Abstract

In this paper, we describe a rough sets approach to classification and attribute extraction of a lymphoma cancer dataset. We verify the classification accuracy of the results obtained from rough sets with a two artificial neural network based classifiers (ANNs). Our primary goal was to produce a classifier and a set of rules that could be used in a predictive manner. The dataset consisted of a number of relevant clinical variables obtained from patients that were suspected of having some form of blood based cancer (lymphoma or leukaemia). Of the 18 attributes that were collected for this patient cohort, seven were useful with respect to outcome prediction. In addition, this study was able to predict with a high degree of accuracy whether or not the disease would undergo metastases.