Interpretable Machine Learning Model for Predicting 1-Year Unplanned Readmissions in Ischemic Stroke Patients with Diabetes: A Synergistic View of Inflammation and Metabolism

Abstract

Background: To develop and validate an interpretable machine learning (ML) model integrating inflammatory and metabolic biomarkers for predicting the risk of 1-year unplanned readmission in patients with ischemic stroke (IS) and type 2 diabetes mellitus (T2DM). Methods: This retrospective study included IS patients with comorbid T2DM who were hospitalized between June 2022 and December 2023. A total of 49 clinical variables were extracted. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. The dataset was randomly divided into a training set (70%) and a validation set (30%). Seven widely used ML algorithms were applied to construct predictive models, and model performance was evaluated using a validation set. No external validation was performed in this study. The best-performing model was further interpreted using Shapley Additive Explanations (SHAP), and a dynamic nomogram was developed for individualized risk assessment. Results: A total of 833 patients were included, with a 1-year unplanned readmission rate of 34.3%. LASSO regression identified nine key variables: age, neutrophil-to-lymphocyte ratio (NLR), homocysteine (HCY), glycated hemoglobin A1c (HbA1c), triglyceride-glucose (TyG) index, metformin use, and the presence of hyperlipidemia, pulmonary infection, and renal insufficiency. The random forest model demonstrated the best overall performance (area under the curve [AUC] = 0.78, F1 score = 0.70). SHAP analysis indicated that NLR, HCY, HbA1c, and TyG index were the most influential predictors, suggesting that chronic inflammation and metabolic dysregulation play pivotal roles in readmission risk. Conclusion: The ML model based on inflammatory and metabolic biomarkers effectively predicts 1-year unplanned readmission risk in IS patients with T2DM, with good interpretability and clinical potential. The dynamic nomogram enables real-time, individualized risk prediction to support early identification of high-risk patients, tailored follow-up, and targeted allocation of healthcare resources.