Comparison of Machine Learning Techniques using the WEKA Environment for Prostate Cancer Therapy Plan

Abstract

The improvement and exploitation of a number of prominent Data Mining techniques in numerous real-world application areas (e.g. Industry, Healthcare and Bioscience) has led to the utilization of such techniques in machine learning environments, in order to extract useful pieces of information of the specified data and support decision making. Throughout this study, a comprehensive techniques' comparison is performed upon a fairly large set of data consisting of real medical incidents of men with the diagnosis of prostate cancer which are receiving medical treatment. 40 patients, suffered previously with prostate cancer and without undergone radiation therapy, were examined for therapy change after already receiving medical treatment. Six parameters were measured for eight subsequent quartiles to assess the patient state and its treatment outcome. Specifically, with the aim of the open source WEKA environment, the given data is tested with a number of machine learning andclassification techniques in order to compare the performance of the chosen algorithms upon the practitioner's decision of a potential therapy change.