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<p>This study aimed to develop a machine learning (ML) model for predicting the risk of acute kidney injury (AKI) in diabetic patients with heart failure (HF) during hospitalization. Using data from 1,457 patients in the MIMIC-IV database, the study identified twenty independent risk factors for AKI through LASSO regression and logistic regression. Six ML algorithms were evaluated, including LightGBM, random forest, and neural networks. The LightGBM model demonstrated superior performance with the highest prediction accuracy, with AUC values of 0.973 and 0.804 in the training and validation sets, respectively. The Shapley additive explanations algorithm was used to visualize the model and identify the most relevant features for AKI risk. Clinical impact curves further confirmed the strong discriminatory ability and generalizability of the LightGBM model. This study highlights the potential of ML models, particularly LightGBM, to effectively predict AKI risk in diabetic patients with HF, enabling early identification of high-risk patients and timely interventions to improve prognosis.</p>

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Machine learning for risk prediction of acute kidney injury in patients with diabetes mellitus combined with heart failure during hospitalization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月28日

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  • Guojing Li,
  • Zhiqiang Zhao,
  • Zongliang Yu,
  • Junyi Liao,
  • Mengyao Zhang

摘要

This study aimed to develop a machine learning (ML) model for predicting the risk of acute kidney injury (AKI) in diabetic patients with heart failure (HF) during hospitalization. Using data from 1,457 patients in the MIMIC-IV database, the study identified twenty independent risk factors for AKI through LASSO regression and logistic regression. Six ML algorithms were evaluated, including LightGBM, random forest, and neural networks. The LightGBM model demonstrated superior performance with the highest prediction accuracy, with AUC values of 0.973 and 0.804 in the training and validation sets, respectively. The Shapley additive explanations algorithm was used to visualize the model and identify the most relevant features for AKI risk. Clinical impact curves further confirmed the strong discriminatory ability and generalizability of the LightGBM model. This study highlights the potential of ML models, particularly LightGBM, to effectively predict AKI risk in diabetic patients with HF, enabling early identification of high-risk patients and timely interventions to improve prognosis.