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

Jialin Liu,Jinfa Wu,Kunchang Hu,Siru Liu,Mengdie Li,Ke Li,Muhammad Adrish

doi:10.1371/journal.pone.0246306

Abstract

PurposeThe 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.MethodsWe 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.ResultsA 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 < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models.ConclusionXGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.

Highlights

Acute kidney injury (AKI) is a common condition with a high mortality rate, morbidity, high cost, and risk of developing chronic kidney disease [1]
The level of diagnosis and treatment has improved in recent years, the burden of disease caused by AKI is still very high, especially in the intensive care unit [1]
Ke Lin et al (2019) used the RF algorithm to build a mortality prediction model, and predicted the mortality risk of AKI patients in ICU. Their model was compared with SVM, ANN, and Customized SAPS-II (Simplified Acute Physiology Score-II) scores [5]

Summary

Introduction

Acute kidney injury (AKI) is a common condition with a high mortality rate, morbidity, high cost, and risk of developing chronic kidney disease [1]. It is a global health issue [2]. Ke Lin et al (2019) used the RF (random forest) algorithm to build a mortality prediction model, and predicted the mortality risk of AKI patients in ICU Their model was compared with SVM (support vector machine), ANN (artificial neural network), and Customized SAPS-II (Simplified Acute Physiology Score-II) scores [5]. XGBoost is a highly efficient boosting ensemble learning model that originated in the decision tree model, which uses the tree classifier for better results of prediction and higher operation efficiency [11, 12]

Objectives

Discussion

Conclusion