  Diagnosis of breast cancer has been well studied in the past. Multiple linear
programming models have been devised to approximate the relationship between
cell features and tumour malignancy. However, these models are less capable in
handling non-linear correlations. Neural networks instead are powerful in
processing complex non-linear correlations. It is thus certainly beneficial to
approach this cancer diagnosis problem with a model based on neural network.
Particularly, introducing bias to neural network training process is deemed as
an important means to increase training efficiency. Out of a number of popular
proposed methods for introducing artificial bias, Bimodal Distribution Removal
(BDR) presents ideal efficiency improvement results and fair simplicity in
implementation. However, this paper examines the effectiveness of BDR against
the target cancer diagnosis classification problem and shows that BDR process
in fact negatively impacts classification performance. In addition, this paper
also explores genetic algorithm as an efficient tool for feature selection and
produced significantly better results comparing to baseline model that without
any feature selection in place
