Application of Data Mining Classification Algorithms for Breast Cancer Diagnosis

Breast cancer is one of the diseases that represent a large number of incidence and mortality in the world. Data mining classifications techniques will be effective tools for classifying data of cancer to facilitate decision-making. The objective of this paper is to compare the performance of different machine learning algorithms in the diagnosis of breast cancer, to define exactly if this type of cancer is a benign or malignant tumor. Six machine learning algorithms were evaluated in this research Bayes Network (BN), Support Vector Machine (SVM), k-nearest neighbors algorithm (Kim), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression. The simulation of the algorithms is done using the WEKA tool (The Waikato Environment for Knowledge Analysis) on the Wisconsin breast cancer dataset available in UCI machine learning repository.