## 4 Discussion and Conclusions
### Conclusions  
In the first place, the literature reviewed revealed that readmissions, hospitalizations, and mortality are frequent in patients with cardiovascular pathologies, especially those with CHD. In addition, it was established that some of the main variables influencing patients' hospitalization, readmission, and mortality are hypertension, systolic pressure, diastolic pressure, heart rate, gender, age, and body mass index. Finally, studies usually analyze cardiovascular disease risk factors and predict pathologies such as cerebrovascular infarction and coronary artery disease. No studies have been observed that associate clinical mortality outcomes with multiple chronic conditions and cardiovascular diseases. This study proposes supervised models to predict patients' mortality risk from cardiovascular variables, medical history, medications, and electrocardiogram variables, including a summary of data that takes into consideration some temporal variation. The accuracy of random forest models was between 0.83 and 0.89, which are similar or, in some cases, better results than those in the literature. These are mostly survival models based on Cox regression or supervised learning with methodologies other than the one implemented. The final conclusion is that the models respond to the objective of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases (MCC).  
This research employed a series of algorithms based on supervised learning that can accurately predict patient mortality using the CHS database. Future work will seek to improve the performance of the models with other preprocessing methods and machine learning models. In addition, the algorithm will be extended to predict other events, such as hospitalization. In addition, different approaches based on deep learning algorithms will be tested to perform the prediction, such as LSTM or artificial neural networks. Finally, more specific studies will be conducted to determine the effect of MCCs in different combinations in predicting mortality, and the models will be tested on other databases so that the models can be adapted to different cases.  
Thus, this study resulted in an algorithm to predict mortality. However, in the future, this algorithm could benefit health professionals in a practical way if the model were implemented through a web or desktop application into which they could enter patient data and receive a prediction.  
Conceptualization, J.P., M.E.L., C.T. and R.L.F.; methodology, J.P., R.L.F., C.T., Q.Z. and M.E.L.; formal analysis, J.P., J.P.N. and R.L.F.; investigation, J.P., C.T., R.L.F., J.P.N. and M.E.L.; writing--original draft preparation, J.P., C.T. and R.L.F.; writing--review and editing, J.P., J.P.N., Q.Z. and R.L.F.; supervision, R.L.F., C.T. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics, Bioethics, and Biosafety Committee of the University of Concepcion (Universidad de Concepcion) in January 2019.  
Not applicable.  
The CHS data used in this article can be obtained from the NHLBI Biologic Specim and Data Repository Information Coordinating Center (BioLINCC).  
The authors would like to thank the National Center on Health Information Systems (CTI220001 CENS), Universidad de Concepcion, FONDECYT Regular 1201787: Multimodal Machine Learning approach for detecting pathological activity patterns in elderlies, and ANID Millennium Science Initiative Program (Millennium Nucleus on Sociomedicine NCS2021_013) for supporting the authors of this work. We also would like to give special thanks to [PERSON] his critical review. This manuscript was prepared using CHS data obtained from the NHLBI Biologic Specim and Data Repository Information Coordinating Center (BioLINCC) and does not necessarily reflect the opinions or views of the CHS or NHLBI.  
The authors declare no conflict of interest.