Abstract: With the increasing aging of the population, the incidence of heart failure has increased, and the problem of unplanned readmission of patients with heart failure has led to a decrease in the quality of life of patients and an increase in medical costs. Therefore, it has become an urgent problem to be solved. Aiming at the problem of readmission risk prediction, this paper proposes an unplanned readmission risk prediction model for heart failure patients based on ADE-Stacking. This model is mainly composed of two parts: integrated learning algorithm model construction and parameter optimization. The integrated learning algorithm can be combined with multiple parts. The advantages of a weak classifier make the model have better generalization and accuracy. The parameter optimization part uses the adaptive shrinkage factor F to improve the differential evolution algorithm to improve the parameter optimization performance. The model is trained and tested using the heart failure readmission patient data set. The results show that the proposed model is better than other machine learning algorithms such as random forest, XGBoost, support vector machine and other commonly used risk prediction models.

Key words: heart failure, readmission, differential optimization algorithm, integrated learning, parameter optimization