Improved Breast Cancer Detection Using Ensembled Machine Learning Models

Abstract

Breast cancer (BC) is one of the most deadly cancers for the women across the world. Early detection of breast cancer is very important for life saving. Though it is one of the most critical diseases, yet permanent cure has not been developed till now. Artificial intelligence (AI) and machine learning (ML) have been playing a vital role for effective and quick detection of this disease and increasing the rate of survivals. Deep learning (DL) technologies are helping for the analysis of very important features affecting prediction and detection of serious breast cancer diseases. This research paper focuses on solving the problem of BC detection using Wisconsin Diagnosis Breast Cancer (WDBC) data set by applying different ML models after training and validation. Additionally, various types of performance metrics have been calculated and studied. Various ensemble models have also been developed for improved detection of BC. Recurrent neural network (RNN) and gated recurrent unit (GRU)-based DL models are used for feature extraction and training. Different classification models used in this literature are random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR). After classification, the majority voting and stacking ensemble models have been applied for better performances. After exhaustive simulation and analysis, the performance measures in terms of accuracy, precision, recall, F1-score and area under the curve (AUC) values of the ensembled model are 97.3%, 0.97, 0.971, 0.97 and 0.974, respectively. The proposed ensembled model gives higher performances compared to the individual ML models.