Machine‐Learning Prediction of Tumor Comorbidity and Cancer‐Associated Mortality in Patients With Coronary Heart Disease: A 2‐Stage Modeling Study
- 1Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
- 2Huazhong University of Science and Technology
- 3Peking University First Hospital
Abstract
BACKGROUND: Coronary heart disease (CHD) with concomitant tumors is increasingly prevalent and is associated with a higher mortality. We aimed to develop 2 predictive models to enable early identification of tumor risk in patients with CHD and mortality risk in those with CHD and cancer. METHODS: We analyzed 134 141 patients with CHD treated at Tongji Hospital (Huazhong University of Science and Technology, Wuhan, Hubei, China) during 2001 to 2023. Data collected from the First Hospital of Peking University (Beijing, China) were used for external validation. We developed and evaluated 6 machine learning models (extreme gradient boosting, light gradient boosting machine, random forest, neural network, decision tree, and logistic regression). Model explainability was improved using Shapley additive explanation, and an online prediction platform was subsequently implemented. RESULTS: For the model of predicting tumor risk in patients with CHD, extreme gradient boosting showed the best performance in the test set (area under the receiver operating characteristic curve =0.935), achieving the lowest Brier score (0.080). Its sensitivity, accuracy, and F1 score were 0.740, 0.882, and 0.751, respectively. Meanwhile, light gradient boosting machine performed well for in-hospital mortality prediction in the test set (area under the receiver operating characteristic curve =0.965), achieving the lowest Brier score (0.019), and the highest sensitivity (0.677), and F1 score (0.599). The 2 models also showed good performance in the external validation, suggesting a degree of generalizability. CONCLUSIONS: We constructed 2 predictive models to assess the risk of tumor and cancer-associated mortality in patients with CHD. The web platform assists clinicians in decision-making and patient management.
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BibTeX
@article{zheng2026machinelearning,
title = {{Machine‐Learning Prediction of Tumor Comorbidity and Cancer‐Associated Mortality in Patients With Coronary Heart Disease: A 2‐Stage Modeling Study}},
author = {Wei Zheng and Hongsen Cai and Yahui Li and Man Huang and Qin Fang and Luyun Wang and Daowen Wang and Jie Jiang and Yan Zhang and Hu Ding and Wenguang Hou and Hongling Zhu and Yan Wang},
journal = {Journal of the American Heart Association},
year = {2026},
month = oct,
doi = {10.1161/jaha.126.051025},
url = {https://www.ahajournals.org/doi/10.1161/JAHA.126.051025},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Machine‐Learning Prediction of Tumor Comorbidity and Cancer‐Associated Mortality in Patients With Coronary Heart Disease: A 2‐Stage Modeling Study
AU - Wei Zheng
AU - Hongsen Cai
AU - Yahui Li
AU - Man Huang
AU - Qin Fang
AU - Luyun Wang
AU - Daowen Wang
AU - Jie Jiang
AU - Yan Zhang
AU - Hu Ding
AU - Wenguang Hou
AU - Hongling Zhu
AU - Yan Wang
PY - 2026
DA - 2026/10/20
JO - Journal of the American Heart Association
DO - 10.1161/jaha.126.051025
UR - https://www.ahajournals.org/doi/10.1161/JAHA.126.051025
AB - BACKGROUND: Coronary heart disease (CHD) with concomitant tumors is increasingly prevalent and is associated with a higher mortality. We aimed to develop 2 predictive models to enable early identification of tumor risk in patients with CHD and mortality risk in those with CHD and cancer. METHODS: We analyzed 134 141 patients with CHD treated at Tongji Hospital (Huazhong University of Science and Technology, Wuhan, Hubei, China) during 2001 to 2023. Data collected from the First Hospital of Peking University (Beijing, China) were used for external validation. We developed and evaluated 6 machine learning models (extreme gradient boosting, light gradient boosting machine, random forest, neural network, decision tree, and logistic regression). Model explainability was improved using Shapley additive explanation, and an online prediction platform was subsequently implemented. RESULTS: For the model of predicting tumor risk in patients with CHD, extreme gradient boosting showed the best performance in the test set (area under the receiver operating characteristic curve =0.935), achieving the lowest Brier score (0.080). Its sensitivity, accuracy, and F1 score were 0.740, 0.882, and 0.751, respectively. Meanwhile, light gradient boosting machine performed well for in-hospital mortality prediction in the test set (area under the receiver operating characteristic curve =0.965), achieving the lowest Brier score (0.019), and the highest sensitivity (0.677), and F1 score (0.599). The 2 models also showed good performance in the external validation, suggesting a degree of generalizability. CONCLUSIONS: We constructed 2 predictive models to assess the risk of tumor and cancer-associated mortality in patients with CHD. The web platform assists clinicians in decision-making and patient management.
SN - 2047-9980
ER -