A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

Abstract

Background: Breast cancer is the second leading cause of death in women. The advent of machine learning (ML) has opened up a world of possibilities for the discovery and formulation of drugs. It is an exciting development that could revolutionize the pharmaceutical industry. By leveraging ML algorithms, researchers can now identify disease-related targets with greater accuracy. Additionally, ML techniques can be used to predict the toxicity and pharmacokinetics of potential drug candidates. Objectives: The main purpose of ML techniques, such as feature selection (FS) and classification, is to develop a learning model based on datasets. Methods: This paper proposed a hybrid intelligent approach using a Binary Grey Wolf Optimization Algorithm and a Self-Organizing Fuzzy Logic Classifier (BGWO-SOF) for breast cancer diagnosis. The proposed FS approach can not only reduce the complexity of feature space but can also avoid overfitting and improve the learning process. The performance of this proposed approach was evaluated on the 10-fold cross-validation technique and the Wisconsin Diagnostic Breast Cancer dataset. Although the performance of breast cancer detection is highly dependent on classification accuracy, most good classification methods have an essential flaw in that they simply seek to maximize the accuracy of classification while ignoring the costs of misclassification among various categories. This is even more important in classification problems when the initial set of features is large. With such a large number of features, it is of special interest to search for a dependency between an optimal number of selected features and the accuracy of the classification model. Results: In experiments, standard performance evaluation metrics, including accuracy, F-measure, precision, sensitivity, and specificity, were performed. The evaluation results demonstrated that the BGWO-SOF approach achieves 99.70% accuracy and 99.66% F-measure, which outperforms other state-of-the-art methods. Conclusions: During the comparison of the results, it was observed that the proposed approach gives better or more competitive results than other state-of-the-art methods. By leveraging the power of ML algorithms and artificial intelligence (AI) and the findings of the current study, we can optimize the selection of natural pharmaceutical products for the treatment of breast cancer and maximize their efficacy.