A Framework for Modeling Critical Success Factors in the Selection of Machine Learning Algorithms for Breast Cancer Recognition

Abstract

Analysis of critical success factors allows software development organizations to focus on the factors to be successful. Selecting and implementing an algorithm for bosom cancer recognition could be hard. In this paper, a framework for modeling and analysis of success factors for the selection of Machine Learning methods used for the recognition of bosom cancer is presented. The objective is to analyze critical success factors in Machine Learning techniques selection for bosom cancer recognition built on Fuzzy Mental Maps. A group of common ML algorithms is presented in conjunction with the success factors. An analysis through measures calculation is presented in a case study. It was concluded that relevant factors for the selection of ML algorithms in the recognition of bosom cancer are: Selection of an ML algorithm according to the results, the study of ML algorithms tested in bosom cancer, obtaining and analyzing algorithm results.