Performance and Evaluation of Data Mining Techniques in Cancer Diagnosis

Abstract

We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA. Several data mining classification techniques were used on the proposed data. There are many classification techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The aim of this paper is to investigate the performance of different classification techniques. The data breast cancer data with a total 286 rows and 10 columns will be used to test and justify the different between the classification methods and algorithm.