چكيده به لاتين
One of the most dangerous cancers among women is breast cancer, which takes many victims every year around the world. Early diagnosis of a tumor can significantly increase a person's chances of survival, which has led to a wealth of research in this area. Due to the limitations and problems of the cancer diagnosis process, methods based on machine learning have become common and effective methods in this field. One of the biggest challenges in developing pattern recognition algorithms for cancerous tumors and determining their type is the dependence of large-scale data and distinguishing their differences and similarities from each other. The principal component analysis (PCA) technique is known as one of the most efficient methods in reducing the size and extraction of pure data with the least dependence in previous research. However, the performance of this technique will face challenges in the face of large amounts of data with high nonlinear dependence and cluster overlap. In this case, the expected solution is to develop this technique using kernel function approaches and fuzzy clustering. By using appropriate kernel functions, it is possible to map data in higher dimensions and effectively, and nonlinear connections between data are largely identified. Fuzzy computing-based techniques also make it possible to avoid making crisp decisions for assigning data to different categories. The fuzzy c-means clustering technique is one of the efficient methods in identifying hidden patterns among data and therefore in this research, in order to manage fuzzy data, the focus of this approach has been used. However, one of the main problems in using fuzzy c-means clustering in the standard mode is the possibility of being trapped in local optimization in the process of achieving optimal clusters. In previous studies, in order to solve this problem, the focus has been on one of the two approaches to developing a fuzzy objective function in kernel form or combining the algorithm with an intelligent optimization approach. In this study, in order to achieve maximum performance, in addition to the development of kernel functions, which have been used to solve the problems of nonlinear dependencies in the data extraction stage, in order to avoid trapping in local optimization while using fuzzy clustering, the combined approach with A group gravitational search optimization algorithm is used. Finally, after extracting and accurately determining the hidden patterns in the primary data warehouse, the output of the work is presented as input to the support vector machine algorithm to decide on the type of tumor identified with the highest possible accuracy.
