Explainable extreme boosting model for breast cancer diagnosis

Tamilarasi Suresh (St. Peter’s Institute of Higher Education and Research)
Tsehay Admassu Assegie (Injibara University)
Sangeetha Ganesan (RMK College of Engineering Technology)
Ravulapalli Lakshmi Tulasi (R.V.R and J.C. College of Engineering)
Radha Mothukuri (Koneru Lakshmaiah Education Foundation)
Ayodeji Olalekan Salau (Afe Babalola University)

International Journal of Electrical and Computer Engineering
Vol 13, No 5: October 2023

This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.