Advanced Optimization Techniques & Its Application in AI-Powered Breast Cancer Classification

Abstract

In this paper, an advanced optimization technique will be used to find the cut-off of base model(s) and meta model along with the weights of the weighted blending. In this work, XGBoost, Random Forest, Logistic Regression have been used as the base model and also K-Fold cross validation has been used to capture the average score of individual base model. Here F-score will be used to assess the goodness of the models. The techniques have been applied for classification of Breast Carcinoma which is the one of the most prevailing diseases that thrives amid the human beings over decades. According to a report, published in March '21, in the web site of WHO, in 2020, about 2.3 million women diagnosed with breast cancer and according to International Agency for Research on Cancer (IARC) in December 2020, breast cancer has overtaken the lung cancer and has reached at the top position as a commonly diagnosed cancer. In order to determine the breast carcinoma, breast tumors are classified into two categories which are tagged as malignant or benign. For this study the WBCD dataset has been used as the dataset that contains 569 records derived from Fine Needle Aspirates (FNA) of human breast masses has no missing value and is a balanced dataset which minimizes the data pre-processing and EDA steps. In the Optimized weighted Blending, the F-1 Score goes maximum 0.99 (approx.) compared to other approaches within our scope.