Assessing Breast Cancer Risk with 5g-Enabled Data-Sensorized Sample Using Random Forest - Soto-Gomez, J; Adasme-Soto, P; Zamorano-Illanes, R (Institute of Electrical and Electronics Engineers Inc., 2023)
Breast cancer necessitates precise classification to facilitate optimal treatment decisions. This study evaluates the performance of the Random Forest and Gradient Boosting algorithms in breast cancer classification. Clinical data obtained from medical centers via 5G-enabled devices were subjected to thorough analysis. Both algorithms underwent training, testing, and evaluation, with performance metrics including accuracy, precision, recall, and the F1 score being employed. Notably, key features such as tumor size and hormone receptor status exhibited significant contributions to the classification process. The implications for enhanced diagnosis and personalized treatment approaches are considerable.The study underscores the remarkable effectiveness displayed by both algorithms, highlighting their potential for practical clinical applications and the subsequent improvement of patient outcomes. The integration of AI and 5G technology holds great promise in the advancement of breast cancer care on a global scale. © 2023 IEEE.