Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method.
Jiun-Chi HuangYi-Chun TsaiPei-Yu WuYu-Hui LienChih-Yi ChienChih-Feng KuoJeng-Fung HungSzu-Chia ChenChao-Hung KuoPublished in: Comput. Methods Programs Biomed. (2020)
Keyphrases
- support vector regression
- random forest
- linear model
- ensemble methods
- regression model
- predictive modeling
- rotation forest
- random forests
- model selection
- least squares
- decision trees
- machine learning methods
- ensemble learning
- base classifiers
- machine learning
- prediction accuracy
- benchmark datasets
- generalization ability
- feature set
- knowledge discovery
- ensemble classifier
- feature selection
- data mining
- support vector machine svm
- base learners
- kernel function
- data mining techniques
- text mining
- subspace methods
- data analysis
- linear regression
- feature subset
- cross validation
- data sets