Bimodal Distribution Removal and Genetic Algorithm in Neural Network for Breast Cancer Diagnosis
This work addresses breast cancer diagnosis for medical applications, but it is incremental as it tests existing methods (BDR and genetic algorithm) on a specific dataset.
The paper tackled breast cancer diagnosis by evaluating Bimodal Distribution Removal (BDR) for neural network training and using genetic algorithm for feature selection, finding that BDR negatively impacted classification performance while genetic algorithm significantly improved results compared to a baseline without feature selection.
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