INFLUENCE OF DISCRETIZATION IN CLASSIFICATION OF BREAST CANCER DISEASE

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dc.contributor.authorSAHEED, Yakub Kayode
dc.contributor.authorAKANNI, Abdulsabur Oluseye
dc.contributor.authorALIMI, Maruf O
dc.contributor.authorUsman-Hamza, Fatima E
dc.date.accessioned2019-10-15T09:22:16Z
dc.date.available2019-10-15T09:22:16Z
dc.date.issued2018
dc.description.abstractBreast cancer (BC) is one of the leading cancers for women when compared to all other cancers. It is a killer disease prominent and most frequent type of cancer affecting women worldwide and is increasing particularly in Africa. The aim of this paper is to investigate the influence of data preprocessing based on dicretization in the classification of BC. Two different classification algorithms Support vector machine-Radial basis function (SVM-RBF) and Adaboost algorithm were employed. We analyzed the BC data available from the Wisconsin dataset from UCI machine learning repository. The experiment was performed in Waikato Environment For knowledge analysis (Weka) software. The experimental results showed that discretized SVM-RBF and discretized Adaboost algorithms outperforms the non-discretized SVM-RBF and nondiscretized Adaboost algorithms in terms of accuracy, precision, recall, f-measure and time taken to build the model.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2447
dc.language.isoenen_US
dc.publisherUNIVERSITY OF PITESTI SCIENTIFIC BULLETIN: ELECTRONICS AND COMPUTERS SCIENCEen_US
dc.subjectBreast canceren_US
dc.subjectSupport vector machine-Radial basis functionen_US
dc.subjectAdaboosten_US
dc.subjectDiscretizationen_US
dc.titleINFLUENCE OF DISCRETIZATION IN CLASSIFICATION OF BREAST CANCER DISEASEen_US
dc.typeArticleen_US

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In this paper, it was revealed that data preprocessing by discretization method increase the accuracy, precision, recall, f-measure and time taken to build the model of SVM-RBF and Adaboost algorithms in determining whether a patient has breast cancer disease or not
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