We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study...
We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the ...
determined based on a set of questions determining the history of falls and fear of falls. The extreme gradient boosting (XGBoost) model was built from gait features to predict the factor affecting the risk of falls. Moreover, the definition of the fall levels was classified into high- and ...
To this aim, we chose to use the eXtreme Gradient Boosting (XGBoost) algorithm59, a widely used state-of-the-art machine learning technique known for its high performance and flexibility. XGBoost belongs to the category of so-called ensemble learning approaches, which is a branch of machine ...
Ashish [27] applied SVM and the extreme gradient boosting method to detect ischemic heart disease using the Z-Alizadeh Sani dataset. Among various ML algorithms, SVM has proven to be one of the most outstanding methods [28]. The main idea of SVM [29] is to establish an optimal decision ...
Bankruptcy prediction model using cost-sensitive extreme gradient boosting in the context of imbalanced datasets In the process of bankruptcy prediction models, a class imbalanced problem has occurred which limits the performance of the models. Most prior research add... W Yotsawat,K Phodong,T Prom...
Learned Loss (LL) to update the loss function as the boosting proceeds. Accuracy of the proposed algorithm i.e. XGBoost with Learned Loss boosting function is evaluated using test/train method, K-fold cross validation, and Stratified cross validation method and compared with the state of the ...
In this paper, based on unmanned aerial vehicles (UAV) images and measured water quality data, the genetic algorithm_extreme gradient boosting (GA_XGBoost) algorithm is used to model water quality parameters in the study area, combined with its characteristics of supporting urban river polymorphism ...
To do so, for each CN one ML model was generated (see above and Materials and Methods for details) employing the extreme gradient boost algorithm48. Performance evaluation of the ML models showed an AUC of the receiving operating characteristic curve of 0.986 for the Ain1 CN model and 0.992...
The eXtreme Gradient Boosting (XGBoost) and RF techniques were used58 with 71.3% accuracy. In61,62,63, the authors used multi-classification techniques with accuracy 89%, 90.2% max accuracy and 89% respectively. Using VGG network architecture in66, the accuracy reached 99.2% with 99.5% as ...