چکیده مقاله
Healthcare providers continue to face challenges in identifying breast cancer malignancy, despite using mammography and magnetic resonance imaging, which have limitations. As a result, there is a growing interest in machine learning (ML) for its precision in diagnosis and outcome prediction. This study utilized various ML algorithms to create models for diagnosing breast cancer malignancy, using data from the Wisconsin Diagnostic Breast Cancer database (WDBC). Logistic regression and support vector machines (SVM) models were employed to predict breast cancer malignancy. Logistic regression identified four key parameters: bland chromatin, bare nuclei, marginal adhesion, and clump thickness. It should be mentioned that SVM had higher accuracy and area under the ROC curve (0.99). Both of ML models effectively predicted breast cancer malignancy based on these attributes, making them valuable tools in clinical settings for predicting breast cancer malignancy.
کلیدواژهها
نویسندگان
Hadi Tabesh
Faculty of Life Science Engineering, College of Interdisciplinary Science and Technologies, University of Tehran, Iran
Elham Ansari
Faculty of Life Science Engineering, College of Interdisciplinary Science and Technologies, University of Tehran, Iran
Ardavan Astanei
Faculty of Life Science Engineering, College of Interdisciplinary Science and Technologies, University of Tehran, Iran
شیوه ارجاع
Tabesh, Hadi and Ansari, Elham and Astanei, Ardavan,1403,Model Generation and Prediction of Breast Cancer Malignancy Using Machine Learning Algorithms,1st International Congress on Cancer Prevention,Zanjan
ارائهشده در
مجموعه مقالات اولین کنگره بین المللی پیشگیری از سرطان28 شهریور 1403 · زنجان