Breast Cancer Classification Using Gradient Boosting Algorithms Focusing
  on Reducing the False Negative and SHAP for Explainability
João Manoel Herrera Pinheiro Marcelo Becker
Cancer is one of the diseases that kill the most women in the world, with
breast cancer being responsible for the highest number of cancer cases and
consequently deaths. However, it can be prevented by early detection and,
consequently, early treatment. Any development for detection or perdition this
kind of cancer is important for a better healthy life. Many studies focus on a
model with high accuracy in cancer prediction, but sometimes accuracy alone may
not always be a reliable metric. This study implies an investigative approach
to studying the performance of different machine learning algorithms based on
boosting to predict breast cancer focusing on the recall metric. Boosting
machine learning algorithms has been proven to be an effective tool for
detecting medical diseases. The dataset of the University of California, Irvine
(UCI) repository has been utilized to train and test the model classifier that
contains their attributes. The main objective of this study is to use
state-of-the-art boosting algorithms such as AdaBoost, XGBoost, CatBoost and
LightGBM to predict and diagnose breast cancer and to find the most effective
metric regarding recall, ROC-AUC, and confusion matrix. Furthermore, our study
is the first to use these four boosting algorithms with Optuna, a library for
hyperparameter optimization, and the SHAP method to improve the
interpretability of our model, which can be used as a support to identify and
predict breast cancer. We were able to improve AUC or recall for all the models
and reduce the False Negative for AdaBoost and LigthGBM the final AUC were more
than 99.41\% for all models.