We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于WGBDT的心衰患者半年内再入院风险预测.
- Authors
徐瑞; 肖海军; 胡琛
- Abstract
In order to solve the problem that existing models for predicting and assessing the risk of readmission in heart failure patients lack interpretability and cannot meet the requirements of clinical application, a WGBDT (Weighted Gradient Boosting Decision Trees)-based model for predicting the risk of readmission in heart failure patients within six months is proposed. This model consists of a risk prediction based on the WGBDT algorithm and an interpretation framework based on the SHAP (SHapley Additive exPlanation) model. On the one hand, the WGBDT risk model completes the training of the base classifier by updating the sample weights. The weighted accumulation of the base classifier that iteratively trains the residual samples updated by classification error rates of the base classifier can obtain a model with better generalization and accuracy. On the other hand, the SHAP interpretability framework uses a combination of Kernel SHAP and clinical medicine prior knowledge to interpret the WGBDT black box model and complete the interpretability of the proposed model. By using a clinical dataset with 2008 heart failure patients from a hospital in Sichuan Province as training set and test set, the results show that the conclusions obtained by the proposed model outperform those obtained by mainstream machine learning algorithms such as GBDT, XGBoost, SVM, DT, Adaboost, et al. At the same time, the interpretability of the proposed model is enhanced by using the SHAP framework and the order of the importance of factors affecting heart failure is identified according to the importance of the features. These provide a scientific reference for doctors to formulate more reasonable treatment plans.
- Publication
Journal of South-Central Minzu University (Natural Science Edition), 2023, Vol 42, Issue 3, p425
- ISSN
1672-4321
- Publication type
Article
- DOI
10.20056/j.cnki.ZNMDZK.20230320