EPD: an integrated modeling technique to classify BC

In the past two decades, Breast Cancer (BC) had found as second most common death and continues to be prone in low-middle income countries. However, in those days there have been a lot of technologies developed and implemented in the medical field so far but still unable to cure this disease completely. Thus, need to be more conscious and design novel techniques that would be able to avoid unnecessary deaths at the early stages. In this study, we have taken key studies of related cells, and risk factors and design a novel EPD (EDA, PCA, and DT) model to classify abnormal cells into either benign (B) or malignant (M). Furthermore, EPD has been designed by combining three major techniques as Exploratory Data Analysis (EDA) to visualize the raw data, principal component analysis (PCA) to select the most promising features, and Decision Tree to predict the disease with these features. These findings show the best novel approach against BC for doctors as well as healthcare organizations as compared to individual techniques.