A Comparative Analysis of Machine Learning Approaches in Endometrial Cancer
Chaitanya Pandey, Nitya Nagpal, Rahul Khurana, Preeti Nagrath · 2023
Endometrial cancer (EC) is a form of uterine cancer known for being fatal and shows a robust therapeutic response if diagnosed at an early stage. The inability of traditional EC approaches to provide timely and inexpensive diagnosis has shifted with the introduction of computational techniques spearheaded by oncologists and data scientists. The growing relevance of machine learning has found application in EC, and this chapter explores the different machine learning algorithms incorporated by researchers. Summarizing and comparing different machine learning approaches that have been used to tackle a range of issues associated with EC provides a forum to list the advantages and disadvantages of each process. To overcome the limitations faced by existing and potential models, this chapter provides a detailed account of different scenarios where adopting machine learning approaches can lead to setbacks owing to improper data set creation or algorithmic shortcomings.