## 1 Introduction
### Related Work  
First, there will be a discussion of the main aspects and challenges of mortality risk prediction in older adult patients with multiple chronic conditions and cardiovascular pathologies. In order to predict mortality, the effect of different variables must be taken into account. [PERSON] et al. conducted a study to show the association between cardiovascular risk and the age and gender of patients. This study showed that age is an important factor in the deterioration of cardiovascular function, resulting in an increased risk of cardiovascular disease (CVD) in older adults. It also showed that the prevalence of CVD increases with age in both men and women, including the prevalence of atherosclerosis, stroke, and myocardial infarction; in fact, the incidence of CVD is 89.3% in men and 91.8% in women in adults older than 80 years. Patients' risk may change depending on other pathologies, such as diabetes, hypertension, and obesity. A high prevalence of heart failure, atrial fibrillation, and other forms of CVD was first noted in elderly patients. The high prevalence of CVD in this population has been linked to many factors, including increased oxidative stress, inflammation, apoptosis, and general myocardial deterioration and degeneration [6]. With the progress of sophisticated machine learning algorithms and advanced data collection and storage technologies, researchers have achieved promising results for predicting the risk of having a single chronic disease. State-of-the-art machine learning algorithms, such as deep neural networks, support vector machines, logistic regression, random forest, and decision trees, have been widely implemented and validated to predict the risk of having chronic diseases, such as hypertension, cardiovascular disease, heart disease, and diabetes mellitus. Much of the research has focused on predicting a single chronic disease, but chronic diseases, especially in old age, often co-occur with other chronic diseases, a phenomenon known as multimorbidity or MCC. The lack of a model for multiple chronic conditions can be attributed to the paucity of a dataset covering MCC. Available datasetsare usually dedicated to a particular type of disease. To address this challenge, [PERSON] et al. designed interpretable machine learning algorithms to tackle the problem of classifying whether or not a participant is at high risk for CCM by identifying associated risk factors and exploring the interactions between them. [PERSON] et al. used the Catapult Health database, which consists of 451,425 records, among which 137,118 records, 77,149 records, and 237,159 records were collected in 2018, 2017, and 2012-2016, respectively. For this study, 301,631 patients were selected, of which 10,427 participants (3.5%) were flagged as high-risk individuals with at least one chronic condition, and 291,204 participants (96.5%) were flagged as low-risk individuals without CCM. Twenty variables were selected for analysis: diastolic blood pressure (DBP), systolic blood pressure (SBP), glucose (GLU), triglycerides (TGS), total cholesterol (TCHOL), body mass index (BMI), low-density lipoprotein (LDL) cholesterol, weight, age, alanine aminotransferase (ALT), blood pressure assessment, abdominal circumference, metabolic syndrome risk, diabetes assessment, and BMI assessment. Seven machine learning algorithms, namely, k-nearest neighbors (kNN), decision tree (DT), random forest (RF), gradient boosting tree (GBT), logistic regression (LR), support vector machine (SVM), and Naive Bayes (NB), were used and evaluated for this study. Regarding the results, the AUC values achieved by the GBT classifier, RF classifier, SVM classifier, DT classifier, NB classifier, LR classifier, and kNN classifier were 0.850, 0.846, 0.833, 0.837, 0.821, 0.823, and 0.826, respectively [7].