چکیده مقاله
Breast cancer, being one of the most commonly diagnosed cancers, poses a significant health challenge globally It accounts for approximately 2 3 million new cases each year, as per the GLOBOCAN 2020 data This study leverages machine learning to enhance early detection and prediction of breast cancer, utilizing a comprehensive dataset of risk factors Normalization was applied on the dataset and then 80% of the data was used for training five ensemble based algorithms: Random Forest, Rotation Forest, LightGBM, XGBoost, and CatBoost These algorithms were assessed using four performance metrics: accuracy, precision, recall, and F1 score CatBoost emerged as the leading algorithm, achieving an impressive accuracy of 87 17%, precision of 86 25%, recall of 87 17%, and F1 score of 86 37% These results highlight CatBoost's superior performance in accurately detecting breast cancer cases, crucial for effective treatment and patient management By integrating advanced machine learning methodologies, this research contributes significantly to improving early detection and diagnostic accuracy, thereby enhancing patient outcomes and supporting better clinical decision making in the management of breast cancer
کلیدواژهها
نویسندگان
Hannah Yousefpour
Dept Computer Engineering, University of Mazandaran
Sekineh Asadi Amiri
Dept Computer Engineering, University of Mazandaran
شیوه ارجاع
Yousefpour, Hannah and Asadi Amiri, Sekineh,1403,Ensemble-Based Breast Cancer Prediction Using Risk Factor Data,1st International Congress on Cancer Prevention,Zanjan
ارائهشده در
مجموعه مقالات اولین کنگره بین المللی پیشگیری از سرطان28 شهریور 1403 · زنجان