## 1 Introduction  
Heart failure (HF), which is a prevalent cardiovascular ailment, leads to the majority of CVD and mortality resulting from the heart's inability to maintain the body's metabolism [1]. HF, which represents the end stage of various cardiovascular diseases, is a significant healthcare concern. The spread of COVID-19 worldwide has exacerbated matters, as HF has emerged as a severe complication that is associated with a poor prognosis and mortality from COVID-19 [2]. HF, a prominent contributor to mortality, affects approximately 6.2 million individuals in the United States, resulting in over 800,000 hospital admissions annually. Hospitalization for HF is associated with an alarming 36% annual mortality rate. Patients diagnosed with HF face an increased risk, with 30-50% experiencing mortality within five years of diagnosis [3]. Despite notable advancements in cardiac care, the prevalence of HF has substantially risen, contributing to persistent high mortality rates and a subsequent escalation in medical expenditures for this condition. Individuals with HF manifest identifiable symptoms, including shortness of breath, peripheral edema, ankle swelling, body fatigue, elevated jugular venous pressure, and pulmonary crackles attributed to either cardiac or noncardiac structural abnormalities. Despite improvements in cardiac care, the ongoing surge in HF cases underscores the urgent need for effective interventions to mitigate mortality rates and alleviate the associated economic burden. The Electronic Health Records (EHRs) are a set of electronic personal health records, including medical records, ECG, medical imaging, etc. The EHR systems have been implemented all over the world due to their continuous up-to-date updates. The availability of electronic clinical data has increased dramatically due to developments in information technology and the use of EHRs. However, a wealth of overlooked patient-related information exists in EHRs, and such information, in part, helps predict patient outcomes [4]. EHRs are the digital version of paper charts. In the past, many researchers used EHR database data to predict patient mortality, hospital admission time, disease diagnosis, disease onset, etc., in order to prevent and intervene early diseases of patients that are critical to intensive care. As a fundamental risk-assessment tool, predictive models have been developed and applied in many areas of healthcare. Widespread implementation of EHRs has sparked a revolution bringing individual patient demographics, genetic profiles, medical, diagnostic records, laboratory results, and image data into electronic formats to facilitate access and use in \"big data\" research investigations. The feasibility of truly personalized and precision medicine depends on the management of the vast amount of available EHR data that are critical to the development of these models [5]. Accurate mortality prediction for ICU patients with heart failure (HF) is vital. It helps medical teams develop effective care plans, prioritize high-risk patients, and optimize resource allocation. It also provides crucial information for patients and their families about disease severity, aiding informed decision-making. Additionally, it supplies valuable data for HF research, advancing treatment methods and preventive measures. Overall, such predictions enhance patient care, resource management, patient education, and research efforts [6; 7]. According to the World Federation of Societies of Intensive and Critical Care Medicine, an ICU is an organized system for the provision of care to critically ill patients that provides intensive and specialized medical and nursing care, an enhanced capacity for monitoring, and multiple modalities of physiologic organ support to sustain life during a period of life-threatening organ-system insufficiency [8]. In the United States, 40% of people die during hospitalization, and approximately 22% of patients spend their entire hospital stay in the ICU [9].