## 2 Materials and Methods
### Data Collection and Preprocessing  
Our investigation utilized the Medical Intensive Care Information Mart (MIMIC-III) database, renowned for its extensive clinical data capturing patients admitted to Beth Israel Deaconess Medical Center in Boston, MA. Acknowledged as a principal repository of de-identified public data for research purposes [21], MIMIC-III encompasses 26 CSV-formatted tables meticulously documenting various aspects of patient data during ICU treatment. This comprehensive dataset encompasses laboratory test results, demographic attributes, microbiological findings, hemodynamic parameters, treatment regimens, and fluid balance, among other parameters. The wealth of clinical information embedded in MIMIC-III renders it an invaluable asset for medical research. Within the scope of this study, we leveraged MIMIC-III to analyze 46,520 patient-specific data elements and 58,976 admission-related data points, encompassing vital signs, medication records, laboratory metrics, and observational entries. Ethical approval for accessing the MIMIC-III database was secured through the completion of the National Institutes of Health (NIH) online course, successful passage of the Human Research Participant Protection Examination, and the submission of an access request (certificate number: 35628530). These measures were implemented to ensure the incorporation of appropriate safeguards for all data employed in this research study.  
This study aimed to prognosticate mortality among patients with HF by leveraging physiological variables sourced from the MIMIC-III database. The selection of predictor variables was informed by prior investigations focused on mortality prediction in HF patients. The considered variables encompassed vital indicators, such as heart rate, respiratory rate, diastolic and systolic blood pressure, body temperature, oxygen saturation, inspired oxygen fraction, blood urea nitrogen, creatinine, mean blood pressure, blood glucose, white blood cells, red blood cells, prothrombin time, International Normalized Ratio, platelets, Glasgow Coma Scale (GCS) scores for eyes, movement, and language [7; 22]. Additionally, the inclusion of comprehensive social and environmental context was augmented  
Figure 1: The detailed process of data extraction.  
by incorporating patient vital signs and personal background information, including age, gender, insurance type, marital status, religion, and race (as detailed in Appendix A). By exclusively considering patients aged 16 years and above, our study diligently avoided potential information leakage and ensured experimental uniformity by utilizing solely the initial ICU admission data for each patient, excluding subsequent admissions. To underscore the early predictive capacity, input to the predictive model was derived from data collected within the first 24 h of patient admission to the ICU. Addressing the challenges posed by imbalanced datasets, particularly prevalent in numerous application domains, is imperative for robust machine-learning outcomes. Mitigating this imbalance is crucial, and Synthetic Minority Over-Sampling Technique (SMOTE) emerges as a potent strategy for rectifying class-distribution disparities [23]. Through the generation of synthetic profiles, SMOTE effectively balances class distributions, enhancing prediction performance. In this study, we applied various SMOTE ratios to address data skewness and create balanced datasets.