Deep learning-based breast cancer disease prediction framework for medical industries

Abstract

Breast cancer is one among the dreadful cancer which is caused due to formation in breast cells. Earlier recognition of breast cancer is most required in the medical field to avoid the dangerous threat to human life. This is carried out in the existing work, namely Predictive Modeling Technique (PMT). Existing work cannot handle the database with noises properly which might lead to inaccurate prediction outcome. These problems are addressed by introducing Deep Learning-based Breast Cancer Disease Prediction Framework (DLBCDPF). The proposed research framework objective is to present the structures for the disease diagnosis in a further accurate way. In this work, feature selection is achieved through optimization algorithm, namely ranking-based bee colony approach by which the most optimal feature is chosen from the training dataset. The fitness values considered in this work for optimal feature selection are F-score values. Each feature’s F-score and N numbers of feature’s F-score are arranged in a descending manner; in addition, feature subset with more than one feature are produced. In this phase, diagnosis of various stomach-related problems is done through a unique hybridized classification methodology. In this hybridization methodology, clustering is accomplished before classification, and data pruning is attained in every classification iteration. This leads to improved classification accuracy owing to efficient diagnosis. The clustering is attained by fuzzy C-means clustering, and classification is done using the improved deep neural network. The entire research analysis is carried out in python platform for breast cancer dataset from which it is substantiated that the suggested research work tends to outperform in an enhanced way than prevailing work.