Topological Data Analysis of Breast Cancer Using the Mapper Algorithm

We use topological data analysis for the breast cancer dataset, namely the Mapper algorithm. The scientific study of mathematics and data science, known as Topological Data Analysis, or TDA, is predicated on the idea that data has form and significance. The Mapper algorithm is a TDA method that associates each point in a data set with a simplicial complex cell to produce a topological representation of the data set. TDA and the Mapper algorithm are introduced in this publication. In this work, we analyzed a data set of breast cancer cells using the Mapper method. The amount of connected components, loops, and holes, among other topological characteristics, can be determined by the mapper method for breast cancer cells. Patterns and correlations in the data that are difficult to see with conventional methods can be found using these topological properties. The study results showed that the Mapper algorithm could identify topological features of the cancer cells that could be used to distinguish between healthy and cancerous cells.