Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise

HIGHLIGHTS

• who: Peng Zheng from the Georgia Southern University, United States have published the research work: Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise, in the Journal: (JOURNAL)
• what: In this study, ML-based models, including XGBoost, logistic regression, LightGBM, Random Forest (RF), support vector machine (SVM), AdaBoost, Gaussian NB (GNB), k-nearest neighbors (KNN), and multi-layer perceptron neural_network (MLP), were developed for predicting HBL-related risk factors. Following the RFE, 15 relevant features were obtained . . .
  

   

  

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