TY - JOUR
T1 - Explainable Artificial Intelligence in Quantifying Breast Cancer Factors
T2 - Saudi Arabia Context
AU - Alelyani, Turki
AU - Alshammari, Maha M.
AU - Almuhanna, Afnan
AU - Asan, Onur
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/5
Y1 - 2024/5
N2 - Breast cancer represents a significant health concern, particularly in Saudi Arabia, where it ranks as the most prevalent cancer type among women. This study focuses on leveraging eXplainable Artificial Intelligence (XAI) techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients. Six distinct models were trained and evaluated based on common performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC score. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were applied. The analysis identified the Random Forest model as the top performer, achieving an accuracy of 0.72, along with robust precision, recall, F1 score, and AUC-ROC score values. Conversely, the Support Vector Machine model exhibited the poorest performance metrics, indicating its limited predictive capability. Notably, the XAI approaches unveiled variations in the feature importance rankings across models, underscoring the need for further investigation. These findings offer valuable insights into breast cancer diagnosis and machine learning interpretation, aiding healthcare providers in understanding and potentially integrating such technologies into clinical practices.
AB - Breast cancer represents a significant health concern, particularly in Saudi Arabia, where it ranks as the most prevalent cancer type among women. This study focuses on leveraging eXplainable Artificial Intelligence (XAI) techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients. Six distinct models were trained and evaluated based on common performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC score. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were applied. The analysis identified the Random Forest model as the top performer, achieving an accuracy of 0.72, along with robust precision, recall, F1 score, and AUC-ROC score values. Conversely, the Support Vector Machine model exhibited the poorest performance metrics, indicating its limited predictive capability. Notably, the XAI approaches unveiled variations in the feature importance rankings across models, underscoring the need for further investigation. These findings offer valuable insights into breast cancer diagnosis and machine learning interpretation, aiding healthcare providers in understanding and potentially integrating such technologies into clinical practices.
KW - artificial intelligence
KW - breast cancer
KW - classification
KW - explainable artificial intelligence
KW - machine learning
KW - Saudi Arabia
UR - http://www.scopus.com/inward/record.url?scp=85193953433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85193953433&partnerID=8YFLogxK
U2 - 10.3390/healthcare12101025
DO - 10.3390/healthcare12101025
M3 - Article
AN - SCOPUS:85193953433
VL - 12
JO - Healthcare (Switzerland)
JF - Healthcare (Switzerland)
IS - 10
M1 - 1025
ER -