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Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model

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  • Jialin Liu
  • Jinfa Wu
  • Siru Liu
  • Mengdie Li
  • Kunchang Hu
  • Ke Li

Abstract

Purpose: The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models. Methods: We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model. Results: A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p

Suggested Citation

  • Jialin Liu & Jinfa Wu & Siru Liu & Mengdie Li & Kunchang Hu & Ke Li, 2021. "Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0246306
    DOI: 10.1371/journal.pone.0246306
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    References listed on IDEAS

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    1. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
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