Federated Cox Proportional Hazards Model with multicentric privacy-preserving LASSO feature selection for survival analysis from the perspective of personalized medicine.
Carlotta MasciocchiBenedetta GottardelliMariachiara SavinoLuca BoldriniAntonella MartinoCiro MazzarellaMariangela MassaccesiVincenzo ValentiniAndrea DamianiPublished in: CBMS (2022)
Keyphrases
- privacy preserving
- proportional hazards model
- variable selection
- feature selection
- survival analysis
- machine learning
- model selection
- dimension reduction
- cross validation
- regression model
- data mining
- survival prediction
- survival data
- breast cancer
- text categorization
- high dimensional
- high dimensionality
- support vector
- feature space
- data mining methods
- text classification
- logistic regression
- kaplan meier
- feature extraction
- data sources
- classification accuracy
- linear program
- knn
- support vector machine
- predictive modeling
- data mining techniques
- knowledge discovery
- evolutionary computing
- computational intelligence
- genetic algorithm
- neural network
- dimensionality reduction