Different Machine Learning Algorithms for Breast Cancer Diagnosis

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. We aimed from this study to compare different classification learning algorithms significantly to predict a benign from malignant cancer in Wisconsin breast cancer dataset. We used the Wisconsin breast cancer dataset to compare five different learning algorithms , Bayesian Network, Naïve Bayes, Decision trees J4.8 , ADTree, and Multi-layer Neural Network along with t-test for the best algorithm in terms of prediction accuracy. The experiment, has shown that Bayesian Network is significantly better than the other algorithms.