Hybrid Geno-Fuzzy Classifier for Breast Cancer Diagnostics - Julaika Begum K. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Although it is more common in women, breast cancer may also strike males, and it has overtaken lung cancer as the second most lethal form of disease in India. Breast cancer has been diagnosed in a growing number of individuals throughout the world for over two decades. Early detection of breast cancer not only lowers the likelihood of disease-causing mortality but also brings the total cost of treatment for the disease down dramatically. The purpose of the Adaptive Geno-Fuzzy Logic Algorithm model is to make an accurate prediction of breast cancer, which will assist medical professionals in correctly detecting and categorising the lumps that are felt in the breast. The model is made up of two different modules: one that selects breast cancer features using rough sets, and another that classifies patients based on fuzzy rules. The application of the adaptive genetic algorithm allows for the optimization of the rules that are created by fuzzy classifiers. As the initial step of the process, rough sets theory is used to determine significant factors that influence breast cancer. In the second step, breast cancer is predicted with the use of the AGFLA classifier. The experimentation is carried out using the datasets that are made accessible to the public by the Wisconsin Breast Cancer Diagnostic (WBCD). Rough set theory is helpful to the model because it reveals structural linkages within data that is imprecise and noisy. This is true for any dataset, but it is especially true for the medical dataset WBCD, which also suffers from noise. The HGFC method has been shown, via experimental study, to perform better than existing detection and classification methods.