Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.
Omneya AttallahAlan KarthikesalingamPeter J. E. HoltMatthew M. ThompsonRob SayersMatthew J. BownEddie C. ChokeXianghong MaPublished in: BMC Medical Informatics Decis. Mak. (2017)
Keyphrases
- survival data
- proportional hazards model
- abdominal aortic aneurysm
- feature selection
- kaplan meier
- survival analysis
- clinical data
- prostate cancer
- neural network
- risk assessment
- text categorization
- neural network model
- feature space
- risk management
- electronic patient record
- support vector
- text classification
- knowledge acquisition
- feature extraction
- machine learning
- risk factors
- databases
- microarray data
- high dimensional data
- model selection
- classification accuracy
- decision making
- medical data
- gene expression data
- medical image analysis
- high dimensionality
- computer aided
- regression model
- data mining
- data sets