Comparison of Conventional and Automated Machine Learning approaches for Breast Cancer Prediction

Abstract

Breast cancer is a type of cancer in which the breast cells grow out of control. It is one of the leading cause for the high pace of death in women. Breast cancer classification is mainly done with the help of Machine Learning (ML) algorithms. In this work, we did a comparative analysis by creating a framework using ML and Auto ML algorithms (genetic programming) to accurately classify the cells in the breast as cancerous or non-cancerous. The work focused on automating and optimizing the algorithms for better prediction of cancerous cells. In Auto ML, Tree- based Pipeline Optimization Tool (TPOT), a genetic programming approach is used for finding the suitable classifiers and to automatically select the significant features and parameter values associated with the classifiers. Wisconsin Breast cancer diagnostic dataset, which comprises of digitized images taken from fine needle aspirate of breast mass has been used in this work. Evaluation based on recall, precision and accuracy have showed good results.