  Precise estimation of cardiac patients' current and future comorbidities is
an important factor in prioritizing continuous physiological monitoring and new
therapies. ML models have shown satisfactory performance in short-term
mortality prediction of patients with heart disease, while their utility in
long-term predictions is limited. This study aims to investigate the
performance of tree-based ML models on long-term mortality prediction and the
effect of two recently introduced biomarkers on long-term mortality. This study
utilized publicly available data from CCHIA at the Ministry of Health and
Welfare, Taiwan, China. Medical records were used to gather demographic and
clinical data, including age, gender, BMI, percutaneous coronary intervention
(PCI) status, and comorbidities such as hypertension, dyslipidemia, ST-segment
elevation myocardial infarction (STEMI), and non-STEMI. Using medical and
demographic records as well as two recently introduced biomarkers, brachial
pre-ejection period (bPEP) and brachial ejection time (bET), collected from 139
patients with acute myocardial infarction, we investigated the performance of
advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and
XGBoost) to predict all-cause mortality within 14 years. The developed ML
models achieved significantly better performance compared to the baseline LR
(C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost,
vs 0.77 for LR) (P-RF<0.001, PAdaBoost<0.001, PXGBoost<0.05). Adding bPEP and
bET to our feature set significantly improved the algorithms' performance,
leading to an absolute increase in C-Statistic of up to 0.03 (C-Statistic, 0.83
for random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, vs 0.74 for LR)
(P-RF<0.001, PAdaBoost<0.001, PXGBoost<0.05). This advancement may enable
better treatment prioritization for high-risk individuals.
