## 1 Introduction  
The intensive care unit (ICU) is a hospital department that provides critical care, nursing, and support to patients who are severely sick [1]. When a patient's serious health concerns necessitate close monitoring, he or she is admitted to the ICU. The ICU is more expensive than ordinary hospital care, since it provides additional health support and uses expensive medical equipment. From the standpoint of Bangladesh, a crisis in the ICU exists in comparison to the overall population, as we saw during the COVID-19 event. Mortality prediction in the ICU might be a helpful choice for ensuring the optimum utilization of ICU resources. This prediction model can also assist clinicians in making decisions prior to assigning ICU services to patients. Those with more serious conditions require additional ICU attention. Patients at low risk, on the other hand, may be discharged from the ICU to make room for dangerously ill incoming patients. Furthermore, in the event of an ICU crisis, the mortality prediction model can assist in identifying patients with a high chance of survival and prioritizing them. In this study, a mortality prediction model is proposed for use in the ICU, with a focus on heart failure patients. Heart failure is a serious heart disorder in which the heart is unable to efficiently pump blood around the body. According to the American Heart Association, the number of persons diagnosed with heart failure is on the rise, with a 46 percent increase expected by 2030, resulting in over 8 million people suffering from the disease [2]. According to a previous study, one out of every five patients with heart failure admitted to a hospital in the United States is admitted to an ICU, a resource-intense environment that accounts for 20-35 percent of overall hospital costs [3]. As a result, as the number of patients with heart failure rises, proper ICU distribution among those patients becomes increasingly crucial. The data on heart failure patients used in this study was gathered from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset [4], which is a publicly available dataset that contains deidentified health data from thousands of ICU admissions. The demographic information, vital signs of heart failure patients, some laboratory variables, and comorbidities were all included in the dataset of heart failure patients used in this study. Background research in the field of ICU mortality prediction demonstrates that there is still room for improvement and development by carrying out comparative studies with feature selection using nature-based algorithms vs. no feature selection. Likewise, research can be extended using explainable AI to determine which features predominate in the prediction process for patients with heart failure in order to make the models more transparent and trustworthy. We conducted our research using these points as research gaps. In this research, we compared the performance of machine learning (ML) models with and without feature selection. Feature selection aids in reducing the curse of dimensionality, as well as speeding up and simplifying the ML method. It can also improve an ML model's performance. Analysis of background studies [5; 6; 7; 8; 9] reveals that nature-based algorithms, namely the flower pollination algorithm (FPA) [10], the Particle swarm algorithm (PSO) [11], and the genetic algorithm (GA) [12] are three of the most effective and utilized algorithms in selecting features. Therefore, in this study, we used certain nature-based algorithms for feature selection. Three nature-based feature selection algorithms, namely the FPA, PSO, and GA, were utilized in this research. Following feature selection, the classification job was carried out using four popular ML models: logistic regression (LR) [13], gradient boosting (GB) [14], random forest (RF) [15], and decision tree (DT) [16].