An Enhanced Filter Based Heterogeneous Data Classification Framework on Mixed Breast Cancer Databases

Abstract

Medical disease classification is one of the major challenges to scientific and medical researchers. Due to its high dimensional feature space and data imbalance, most of the medical databases contain many homogeneous or heterogeneous features. It is difficult to predict the disease label due to class imbalance. Feature transformation, feature ranking and data classification are the essential approaches used to classify the high dimensional data with a high true positive rate. Feature transformation helps to improve the feature ranking process in high dimensional feature space. Most traditional feature transformation approaches such as min-max variance, probabilistic normalization and min-max normalization, etc., are independent of data distribution and multi-class labels. In this work, a novel feature selection based random forest classifier is proposed to improve the efficiency of the medical datasets. Practical results proved that the proposed heterogeneous classification framework has better accuracy, nearly 98.6 % accuracy compared to the traditional nominal data classification models.