Breast cancer is a major disease identified in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Machine Learning (ML) has acknowledged much interest in the hope of providing correct results, but due to irrelevant features, its modelling methodologies and prediction performance are still a difficulty. To solve this issue, Feature Selection (FS) was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced aimed at selecting a subset of features that increase the prediction performance of subsequent classification models while also simplifying their interpretability. BIEFS algorithm uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC).This BIEFS algorithm can improve the accuracy of analysis (benign and malignant).

Breast cancer is a major disease diagnosed in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC). Ensemble Multiple Deep Learning (EMDL) classifier is introduced which combines several individual models such as AdaBoost-Convolutional Neural Network (A-CNN), Long Short-Term Memory Network (LSTM), and Deep Auto-Encoder (DAE) is introduced to obtain better generalization performance. Then, these three classifiers such as A-CNN, LSTM, and DAE are used for ensemble classification using a Weight Majority Voting (WMV) mechanism. Wisconsin Diagnosis Breast Cancer (WOBC), and Wisconsin Diagnosis Breast Cancer (WDBC) are collected from University of California, Irvine (UCI) repository for experimentation. Evaluation metrics like Precision, Recall, F-measure, Accuracy, and Area Under Curve (AUC) are used to compare the results of proposed system and existing classifiers. All experiments are executed within a simulation environment and conducted in MATrix LABoratory R 2014 a (MATLAB 2014a) tool. The proposed EMDL classifier is showing better performance as compared to the traditional classification models.