Using machine learning, general regression, and cox proportional hazards regression to predict the effectiveness of treatment in patients with breast cancer.
Xiaoyan WangDawn L. HershmanAlfred I. NeugutPublished in: AMIA (2006)
Keyphrases
- survival analysis
- breast cancer
- machine learning
- breast cancer patients
- cancer patients
- regression model
- proportional hazards model
- clinical diagnosis
- disease progression
- breast cancer diagnosis
- prognostic factors
- survival data
- cancer treatment
- logistic regression
- computer aided diagnosis
- diagnosis of breast cancer
- risk assessment
- early detection
- predictive modeling
- support vector
- model selection
- computer aided detection
- decision trees
- bayesian networks
- data mining
- evolutionary computing
- breast tissue
- outcome prediction
- cad systems
- breast cancer detection
- data mining methods