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Ke Wang, Jing Tian, Chu Zheng, Hong Yang, Jia Li Ren, Yanling Liu, Qinghua Han, Yanbo Zhang
BACKGROUND: This study sought to evaluate the performance of machine learning (ML) models and establish an explainable ML model with good prediction of 3-year all-cause mortality in patients with heart failure (HF) caused by coronary heart disease (CHD). METHODS: We established six ML models using follow-up data to predict 3-year all-cause mortality. Through comprehensive evaluation, the best performing model was used to predict and stratify patients. The log-rank test was used to assess the difference between Kaplan-Meier curves. The association between ML risk and 3-year all-cause mortality was also assessed using multivariable Cox regression. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to calculate 3-year all-cause mortality risk and to generate individual explanations of the model's decisions. RESULTS: The best performing extreme gradient boosting (XGBoost) model was selected to predict and stratify patients. Subjects with a higher ML score had a high hazard of suffering events (hazard ratio [HR]: 10.351; P < 0.001), and this relationship persisted with a multivariable analysis (adjusted HR: 5.343; P < 0.001). Age, N-terminal pro-B-type natriuretic peptide, occupation, New York Heart Association classification, and nitrate drug use were important factors for both genders. CONCLUSIONS: The ML-based risk stratification tool was able to accurately assess and stratify the risk of 3-year all-cause mortality in patients with HF caused by CHD. ML combined with SHAP could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of key features in the model.
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Wang, K., Tian, J., Zheng, C., Yang, H., Ren, J. L., Liu, Y., Han, Q., & Zhang, Y. (2021). Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP. Computers in Biology and Medicine, 137, 104813. https://doi.org/10.1016/j.compbiomed.2021.104813
Wang, Ke, et al. "Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP." Computers in Biology and Medicine, vol. 137, 2021, pp. 104813, https://doi.org/10.1016/j.compbiomed.2021.104813.
Wang, Ke, Jing Tian, Chu Zheng, Hong Yang, Jia Li Ren, Yanling Liu, Qinghua Han, and Yanbo Zhang. "Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP." Computers in Biology and Medicine 137 (2021): 104813. https://doi.org/10.1016/j.compbiomed.2021.104813.
Wang K., Tian J., Zheng C. [и др.] Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP // Computers in Biology and Medicine. — 2021. — Vol. 137. — P. 104813. — URL: https://doi.org/10.1016/j.compbiomed.2021.104813
@article{wang2021interpretable,
title = {{Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP}},
author = {Ke Wang and Jing Tian and Chu Zheng and Hong Yang and Jia Li Ren and Yanling Liu and Qinghua Han and Yanbo Zhang},
journal = {Computers in Biology and Medicine},
year = {2021},
volume = {137},
pages = {104813},
doi = {10.1016/j.compbiomed.2021.104813},
url = {https://doi.org/10.1016/j.compbiomed.2021.104813}
}TY - JOUR AU - Wang, Ke AU - Tian, Jing AU - Zheng, Chu AU - Yang, Hong AU - Ren, Jia Li AU - Liu, Yanling AU - Han, Qinghua AU - Zhang, Yanbo TI - Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP JO - Computers in Biology and Medicine VL - 137 SP - 104813 PY - 2021 DA - 2021/08/28 DO - 10.1016/j.compbiomed.2021.104813 UR - https://doi.org/10.1016/j.compbiomed.2021.104813 ER -
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