## 1 Introduction
### Related Work
In another study by [PERSON] et al., 85,361 patients were selected, and three machine learning models, XGBoost, artificial neural network, and logistic regression, were built to predict mortality based on different cardiac variables and chronic conditions. Regarding the results, the AUC values were 0.811 for XGBoost, 0.774 for neural networks, and 0.772 for logistic regression models, indicating that the predictive capacity of XGBoost was the highest. This study showed that the machine-learning-based model has a higher predictive ability than the conventional logistic regression model and can be helpful in risk assessment and health counseling for health checkup participants [8]. In order to design the algorithm, a review of previous studies was carried out to establish the main variables affecting mortality and other medical events and the algorithms and supervised learning methods used in these situations. [PERSON] et al. and [PERSON] et al. performed a study to identify the cardiovascular characteristics, comorbidities, and laboratory measures associated with in-hospital mortality in European countries [9,10]. The random forest model used by the authors found that among the most important variables for predicting mortality were impaired renal function, elevated C-reactive protein, advanced age, diabetes, hypertension, and myocardial infarction. [PERSON]'s multivariate survival analysis confirmed these results. Another study by [PERSON] et al. [11] used the information from 529 patients from the CHS database to describe the effect of social support on survival after heart failure. The authors built a [PERSON] survival model using the following as variables: age, gender, race (white or black), medications (antihypertensive, oral hypoglycemic, and lipid-lowering), coronary heart disease status (defined as myocardial infarction, angina, coronary artery bypass grafting, or angioplasty), systolic and diastolic blood pressure, cystatin-C, general health status (excellent, good, fair, or poor), self-reported limitations in activities of daily living (ADLs), body mass index, and physical activity. The model results showed that participants with high scores in social networks tend to have a higher body mass index (BMI), fewer depressive symptoms, and better interpersonal support scores. Another interesting result was that participants with a high social support score had a 29% lower mortality rate than those with a low score. These findings suggest that structural social support prior to diagnosis may modestly buffer mortality in patients with heart failure. In [12, 13, 14], to analyze risk factors for hospital readmission, a study was conducted with 100 patients and Chi-square tests. The results showed that the most influential variables in hospital readmission were noncompliance with medication (25.8%), poor dietary compliance (22.6%), uncontrolled diabetes mellitus (22.6%), ischemia (19.4%), anemia (16.1%) and worsening left ventricular function (16.1%). The most used supervised learning methods are decision-tree-based algorithms such as random forest, support vector machine, and Naive Bayes and survival algorithms such as Cox regression, as shown in the studies conducted by [PERSON] et al., [PERSON] et al., and [PERSON] et al. [14; 15; 16]. The variables commonly used to train the mentioned models are demographic variables, laboratory examination data, and clinical, cardiovascular, and electrocardiogram variables. Finally, [PERSON] et al. [17] present a deep learning algorithm to predict the deterioration of patients' health. This DeepSigns model predicts vital signs using LSTM networks and calculates the prognostic index for the early diagnosis of patients' worsening health status. To construct the predictor, the authors used fifteen attributes, such as temperature, blood pressure, heart rate, oxygenation, hematocrit, and age, among others.