Abstract:
Breast cancer is a disease usually found in women
which poses serious health challenges and can be fatal if not
diagnosed quickly and treated immediately. Techniques of data
mining have been defined to play a significant role in the
diagnosis of numerous diseases in which breast cancer diagnosis
is a good example. This paper employed homogenous ensemble
on methods of data mining for breast cancer diagnosis. Three
data mining classification algorithms: k-nearest neighbour,
Decision Tree (C4.5) and Support Vector Machines (SVM) with
their homogenous ensembles of Bagging and Boosting were
applied. The experimental result showed that support vector
machines possess the highest classification accuracy,
homogenous ensembles of bagging and boosting does not affect
classification accuracy greatly as they either slightly increase or
reduce the accuracy of classification while increasing the time it
takes for the algorithm to build its model.
