## 2 Background Studies
### Machine-Learning-Based Mortality Prediction  
[PERSON] et al. [34] utilized the RF algorithm to develop a model for predicting mortality for acute renal patients in the ICU. They compared the model's performance to that of the customized simplified acute physiology score (SAPS) II model and two other ML models. They discovered that in comparison to other models, theirs had the best accuracy and discrimination, as evaluated by Brier score and AUROC. [PERSON] et al. [35] are a group of researchers who proposed a novel approach using XGBoost and least absolute shrinkage and selection operator (LASSO) regression; the researchers were able to identify independent risk variables for in-hospital mortality in ICU-admitted heart failure patients. The calibration of the XGBoost and LASSO regression models was also quite good. According to this study, in a paired comparison, the XGBoost and LASSO regression models achieved better predicted performance than the Guidelines-Heart Failure (GWTG-HF) risk score model. [PERSON] et al. [36] suggested a DELAK (dynamic ensembling learning algorithm based on k-means) to predict ICU mortality. In most mortality prediction tests, DELAK outperformed six other fusion processes; traditional scoring systems; classical ensemble models including AdaBoost, bagging, and RF; and dynamic ensemble selection approaches in terms of AUROC and Area under the rust progress curve (AURPC). [PERSON] et al. [37] used different ML algorithms to present an ensemble strategy to predict ICU patient mortality. The study's model exceeded state-of-the-art approaches in terms of accuracy according to the findings (94.4%). [PERSON] et al. [38] proposed a novel hybrid prediction model based on a combination of stacking and boosting ensemble methods and a new ensemble classifier based on the GA as a feature selection methodology. The suggested model was compared to the APACHE and SAPS scoring systems for experimental validation, and it outperformed the state-of-the-art models, yielding promising results. To effectively manage COVID-19 patients admitted to a medical unit, [PERSON] et al. [39] developed a method to identify early prognostic indicators upon arrival. Using multivariable LR models, the predictors of ICU transfer or death on day 14, as well as being released alive or in a severe state on day 14 (remaining on ventilation or death), were investigated. [PERSON] et al. [40] compared the Combined Assessment of Risk Encountered in Surgery (CARES) model with the American Society of Anesthesiologists Physical Status (ASA-PS) in terms of predicting 30-day post-surgical mortality and the necessity for a more than 24 h ICU stay. With AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes, respectively, GB was shown to be the best-performing model to develop forecasting models. [PERSON] et al. [41] used the LASSO, RF, and gradient boosting machine (GBM) algorithms, as well as the standard LR method. In terms of prediction, the ML-based models developed in this study performed well. The GBM model performed the best in terms of forecasting the chance of in-hospital death according to this study. In a study of COVID-19 patients, [PERSON] et al. [42] evaluated the effectiveness of 18 ML methods for forecasting ICU admission and death. Ensemble-based models outperformed other model types in predicting COVID-19 5-day ICU admission and 28-day mortality according to this study. [PERSON] et al. [43] developed an ML prediction model based on COVID-19 patient demographics, clinical factors, comorbidities, and biochemical markers. This model was able to predict COVID-19 hospital mortality with moderate predictive power, with Q2 equal 0.24 and high-accuracy AUROC greater than 0.85 by discriminating non-survivors from survivors using training and validation data. [PERSON] et al. [44] utilized easy and widely scalable ML-based techniques to forecast mortality in real time during traumatic brain injury in critical care.