Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniques

Abstract

In the last two decades, regardless of epidemiological, and clinical studies, the incidence of breast cancer (BC) is still increasing. However, so far, a lot of research has been done in this field to diagnose BC, and some of them have been discussed in the literature section. But still, happening major issues while dealing with fault feature matrix, generated from traditional feature extraction methods. As a result, the complexity of fault classification has raised, which will negatively impact fault identification’s accuracy and effectiveness. Thus, in this research, a novel hybridized machine learning-fuzzy and dimension reduction (MLF-DR) model has been proposed to improve the decision capabilities and efficiency of an ML model. A feature-based class-togetherness fuzzification method has been used for every feature. The novelty of our research work is to find all possibilities between cancerous and non-cancerous cells by implementing a fuzzy inference system (FIS) in the data analysis phase, and DR techniques at preprocessing phase to select the best optimizing features. This research tries to reduce the incidence of BC and prevent needless deaths, thus will probably follow necessary action to perform i.e. (i) FIS to interpret input values; (ii) principal component analysis (PCA), and recursive feature elimination (RFE) to select best features, and (ii) logistic regression (LR) and random forest (RF) models to predict BC with these features. Furthermore, all the experiments have been done on Wisconsin Breast Cancer Dataset (WBCD), freely available on the Kaggle repository using Python programming on Jupyter Notebook version 6.4.3. The key findings of this research are that the LR-PCA (8 components) model can reliably and successfully obtain the defect diagnosis results with 99.1% accuracy, as compared to individual LR and RF models.