Predicting 90-day readmission for patients with heart failure: a machine learning approach using XGBoost

Background: Heart failure (HF) is one of the most prevalent diseases in China and worldwide with poor prognosis. A prognostic model for predicting readmission for patients with HF could greatly facilitate risk stratification and timely identify high-risk patients. Various HF prediction models have been developed worldwide; however, there is few prognostic models for HF among Chinese populations. Thus, we developed and tested an eXtreme Gradient Boosting (XGBoost)model for predicting 90-day readmission for patients with HF. Methods: Clinical data for 1,532 HF patients retrospectively admitted to Zigong Fourth People’s Hospital in Sichuan Province from December 2016 to June 2019 were used to develop and test two prognostic models: XGBoost and logistic models. The least absolute shrinkage and selection operator (LASSO) regression method was applied to filter variables and select predictors. The XGBoost model tuning was performed in a 10-fold cross validation and tuned models were validated in test set (7:3 random split). The performance of the XGBoost model was assessed by accuracy (ACC), kappa, area under curve ( AUC ) and other metrics, and was compared with that of the logistic model. Results: systolic blood pressure, diastolic blood pressure, type of HF, mean corpuscular hemoglobin concentration, total cholesterol were screened out as predictors through LASSO regression. In training set, we optimized four major parameters, max depth, eta, nrounds and early stopping rounds with optimal values of 6, 0.5, 1000 and 5 for XGBoost. In test set, we obtained a ACC of 0.99 with kappa of 0.98 and the AUC , sensitivity and specificity achieved were of 1.00, 1.00 and 0.99 in the XGBoost model, which has significantly higher prediction performance than the logistic model. Conclusion: The XGBoost model developed in our study had excellent prediction performance in test set and the model can contribute to the assessment of 90-day readmission risk for patients with HF in Chinese population.