Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU

• Published in: Journal of stroke and cerebrovascular diseases Vol. 33; no. 7; p. 107729
• Main Authors: Lu, Xiaochi, Chen, Yi, Zhang, Gongping, Zeng, Xu, Lai, Linjie, Qu, Chaojun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-07-2024
• Subjects: Acute kidney injury; Cerebral infarction; External verification; Machine learning; SHapley Additive exPlanations; Cerebral infarction; Acute kidney injury; SHapley Additive exPlanations; External verification; Machine learning
• ISSN: 1052-3057; 1532-8511
• DOI: 10.1016/j.jstrokecerebrovasdis.2024.107729

Acute kidney injury (AKI) is not only a complication but also a serious threat to patients with cerebral infarction (CI). This study aimed to explore the application of interpretable machine learning algorithms in predicting AKI in patients with cerebral infarction.
The study included 3920 patients with CI admitted to the Intensive Care Unit and Emergency Medicine of the Central Hospital of Lishui City, Zhejiang Province. Nine machine learning techniques, including XGBoost, logistics, LightGBM, random forest (RF), AdaBoost, GaussianNB (GNB), Multi-Layer Perceptron (MLP), support vector machine (SVM), and k-nearest neighbors (KNN) classification, were used to develop a predictive model for AKI in these patients. SHapley Additive exPlanations (SHAP) analysis provided visual explanations for each patient. Finally, model effectiveness was assessed using metrics such as average precision (AP), sensitivity, specificity, accuracy, F1 score, precision-recall (PR) curve, calibration plot, and decision curve analysis (DCA).
The XGBoost model performed better in the internal validation set and the external validation set, with an AUC of 0.940 and 0.887, respectively. The five most important variables in the model were, in order, glomerular filtration rate, low-density lipoprotein, total cholesterol, hemiplegia and serum kalium.
This study demonstrates the potential of interpretable machine learning algorithms in predicting CI patients with AKI.