The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients’ demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.

重症监护病房 (ICU) 是所有医院中为重病患者提供的最昂贵和最重要的部分之一。本研究旨在预测死亡率并探讨影响死亡率的关键因素。通常，在医疗保健系统中，对患者进行快速准确的 ICU 死亡率预测对提高护理质量起着关键作用，从而降低成本并提高患者的生存机会。在这项研究中，我们使用了一个医学数据集，包括患者的人口统计详细信息、基础疾病、实验室障碍和 LOS。由于需要准确的估计才能获得最佳结果，因此这里使用各种数据预处理作为初始步骤。此外，还采用机器学习模型来预测 ICU 出院死亡率的风险。对于 AdaBoost 模型，这些度量被认为是 AUC= 0.966，sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% 使得AdaBoost占比最高。我们的模型通过使用各种数据处理场景优于其他比较模型。获得的结果表明，高死亡率可能是由基础疾病引起的，例如糖尿病和高血压、中度肺栓塞 Wells 评分风险、血小板血细胞计数低于 100000 (mcl)、高血压 (HTN)、高胆红素水平、吸烟，GCS 水平在 6 到 9 之间。