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

J Li, Z Zhou, J Dong, Y Fu, Y Li, Z Luan, X Peng - PloS one, 2021‏ - journals.plos.org
Background 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 …

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients

H Dammu, T Ren, TQ Duong - Plos one, 2023‏ - journals.plos.org
The goal of this study was to employ novel deep-learning convolutional-neural-network
(CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and …

Review of intelligent algorithms for breast cancer control: a Latin America perspective

JM Valencia-Moreno… - IEEE Latin America …, 2023‏ - ieeexplore.ieee.org
Breast cancer in women is a worldwide health problem that is one of the main causes of
death. This situation is accentuated in Latin America and the Caribbean countries, where …

[HTML][HTML] Predicting patterns of distant metastasis in breast cancer patients following local regional therapy using machine learning

A Shiner, A Kiss, K Saednia, KJ Jerzak, S Gandhi, FI Lu… - Genes, 2023‏ - mdpi.com
Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which
there is no cure. Here, statistical and machine learning (ML) models were developed to …

Breast Cancer Survival Prediction and Analysis

RK Peddarapu, V Nunemuntala… - … and Smart Systems …, 2023‏ - ieeexplore.ieee.org
The root cause of breast cancer is due to an inherited mutation in the BRCA (Breast Cancer)
gene. The mutated types of the genes can cause abnormal cell growth in the breast which …

[PDF][PDF] Prediction of survival in breast cancer patients using Random Forest classifier and ReliefF feature selection method

DA de Queiroz, GSA Assunção… - International Journal of …, 2021‏ - academia.edu
Studies have evaluated the use of machine learning to support clinical evaluation in cancer
patients. Random Forest has demonstrated to be a relevant technique in predicting survival …