Predicting breast cancer 5-year survival using machine learning: A systematic review

• Published in: PloS one Vol. 16; no. 4; p. e0250370
• Main Authors: Li, Jiaxin, Zhou, Zijun, Dong, Jianyu, Fu, Ying, Li, Yuan, Luan, Ze, Peng, Xin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.04.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Bias; Biology and Life Sciences; Breast cancer; Calibration; Care and treatment; Computer and Information Sciences; Criteria; Data processing; Economic impact; Evaluation; Feature selection; Health services; Informatics; Information processing; Learning algorithms; Literature reviews; Machine learning; Medical prognosis; Medical treatment; Medicine and Health Sciences; Missing data; Model testing; Nursing schools; Patients; People and Places; Performance evaluation; Physical Sciences; Physicians; Prediction models; Preprocessing; R&D; Research & development; Research and Analysis Methods; Survival; China

Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of thi...