Abstract: Breast cancer affects many people at the present time. The factors that cause this disease are many and cannot be easily determined. Additionally, the diagnosis process which determines whether the cancer is benign or malignant also requires a great deal of effort from a doctors and physicians. When several tests are involved in the diagnosis of breast cancer, such as clump thickness, uniformity of cell size, uniformity of cell shape,…etc, the ultimate result may be difficult to obtain, even for medical experts. This has given a rise in the last few years to the use of machine learning and Artificial Intelligence in general as diagnostic tools. In this paper, we analyses the performance of Naïve Baysien Classifier and C5.0 algorithm in predicting the survivable rate of breast cancer patients. The data set used for analysing is the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia database to evaluate the proposed system performances. These techniques helps the physician to take decisions on prognosis of breast cancer patients. At the end of analysis, C5.0 proves better performance than Naïve Baysien Classifier.
Keywords: Breast cancer, Naïve Baysien, C5.0 algorithm.
Title: Predicting Breast Cancer Survivability Using Naïve Baysien and C5.0 Algorithm
Author: Mr. D R Umesh, Thilak C R
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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