Explainable Machine Learning for Breast Cancer Diagnosis.

Cancer is already the leading cause of death in most Brazilian cities and in the world. The understanding of its internal mechanisms and the design of computational models capable of improving its diagnosis will have strong benefits for humanity. New technologies have made available a wealth of data, which can be used to improve the diagnosis of cancer. As a manual analysis of this data is impracticable, many black-box machine learning algorithms have been employed successfully for cancer diagnosis. Despite their high accuracy prediction abilities, black-box models sacrifice transparency and accountability. In contrast, interpretable machine learning algorithms are powerful tools for understanding the underlying mechanism present within a large corpus of data. In this work, Linear Projections and Radviz were used as visualization techniques for data exploration and feature selection. Further, Decision Tree induction algorithms were used to create models that are able to differentiate between Malignant and Benign breast tumors from breast mass images. These models can be considered white-box models which means their inner workings are easier to explain and interpret. The result shows Classification and Regression Trees achieved an accuracy of 96% in predicting breast cancer.