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Diagnosis of breast cancer has been well studied in the past. Multiple linearprogramming models have been devised to approximate the relationship betweencell features and tumour malignancy. However, these models are less capable inhandling non-linear correlations. Neural networks instead are powerful inprocessing complex non-linear correlations. It is thus certainly beneficial toapproach this cancer diagnosis problem with a model based on neural network.Particularly, introducing bias to neural network training process is deemed asan important means to increase training efficiency. Out of a number of popularproposed methods for introducing artificial bias, Bimodal Distribution Removal(BDR) presents ideal efficiency improvement results and fair simplicity inimplementation. However, this paper examines the effectiveness of BDR againstthe target cancer diagnosis classification problem and shows that BDR processin fact negatively impacts classification performance. In addition, this paperalso explores genetic algorithm as an efficient tool for feature selection andproduced significantly better results comparing to baseline model that withoutany feature selection in place

Bimodal Distribution Removal and Genetic Algorithm in Neural Network for Breast Cancer Diagnosis