Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data.
Simon LyraSteffen LeonhardtChristoph Hoog AntinkPublished in: CinC (2019)
Keyphrases
- random forest
- clinical data
- decision trees
- ensemble classifier
- fold cross validation
- feature set
- imbalanced data
- cancer classification
- random forests
- machine learning
- feature selection
- medical data
- feature vectors
- patient data
- support vector machine
- imbalanced datasets
- text classification
- databases
- cancer patients
- prediction accuracy
- ensemble methods
- raw data
- feature extraction
- classification accuracy
- decision tree learning algorithms
- support vector
- support vector machine svm
- ensemble learning
- statistical analysis
- feature space
- supervised learning
- class imbalance
- machine learning methods
- machine learning algorithms
- knowledge discovery
- domain knowledge
- prior knowledge
- image classification
- base classifiers
- mr images