Jiaxin Zan,1,2 Xiaojing Dong,1,2 Hong Yang,1,2 Jingjing Yan,1,2 Zixuan He,3 Jing Tian,3 Yanbo Zhang1,2,4 

1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China; 2Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, People’s Republic of China; 3Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China; 4School of Health Services and Management, Shanxi University of Chinese Medicine, Taiyuan, People’s Republic of China

Correspondence: Jing Tian; Yanbo Zhang, School of Public health, Shanxi Medical University, 56 Xinjian Road, Taiyuan, Shanxi Province, People’s Republic of China, Tel/Fax +86 15535406059, Email 1105551933@qq.com; sxmuzyb@126.com

Purpose: This study sought to develop an unbalanced-ensemble model that could accurately predict death outcomes of patients with comorbid coronary heart disease (CHD) and hypertension and evaluate the factors contributing to death.
Patients and Methods: Medical records of 1058 patients with coronary heart disease combined with hypertension and excluding those acute coronary syndrome were collected. Patients were followed-up at the first, third, sixth, and twelfth months after discharge to record death events. Follow-up ended two years after discharge. Patients were divided into survival and nonsurvival groups. According to medical records, gender, smoking, drinking, COPD, cerebral stroke, diabetes, hyperhomocysteinemia, heart failure and renal insufficiency of the two groups were sorted and compared and other influencing factors of the two groups, feature selection was carried out to construct models. Owing to data unbalance, we developed four unbalanced-ensemble prediction models based on Balanced Random Forest (BRF), EasyEnsemble, RUSBoost, SMOTEBoost and the two base classification algorithms based on AdaBoost and Logistic. Each model was optimised using hyperparameters based on GridSearchCV and evaluated using area under the curve (AUC), sensitivity, recall, Brier score, and geometric mean (G-mean). Additionally, to understand the influence of variables on model performance, we constructed a SHapley Additive explanation (SHAP) model based on the optimal model.
Results: There were significant differences in age, heart rate, COPD, cerebral stroke, heart failure and renal insufficiency in the nonsurvival group compared with the survival group. Among all models, BRF yielded the highest AUC (0.810; 95% CI, 0.778– 0.839), sensitivity (0.990; 95% CI, 0.981– 1.000), recall (0.990; 95% CI, 0.981– 1.000), and G-mean (0.806; 95% CI, 0.778– 0.827), and the lowest Brier score (0.181; 95% CI, 0.178– 0.185). Therefore, we identified BRF as the optimal model. Furthermore, red blood cell count (RBC), body mass index (BMI), and lactate dehydrogenase were found to be important mortality-associated risk factors.
Conclusion: BRF combined with advanced machine learning methods and SHAP is highly effective and accurately predicts mortality in patients with CHD comorbid with hypertension. This model has the potential to assist clinicians in modifying treatment strategies to improve patient outcomes.

Keywords: coronary heart disease comorbid with hypertension, ensemble learning, balanced random forest, SHAP, Prognosis