重症监护病房(ICU)是所有医院中最昂贵和最重要的部分。这项研究旨在预测死亡率并探索影响死亡率的关键因素。一般来说,在医疗保健系统中,对患者进行快速准确的ICU死亡率预测对护理质量起着关键作用,从而降低了成本并提高了患者的生存机会。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细节,潜在的疾病,实验室紊乱,还有LOS.由于需要准确的估计才能获得最佳结果,这里使用各种数据预处理作为初始步骤。此外,机器学习模型用于预测ICU出院的死亡风险。对于AdaBoost模型,这些指标被认为是AUC=0.966,灵敏度(召回)=87.88%,Kappa=0.859,F-measure=89.23%,AdaBoost,占最高比率。通过使用各种数据处理方案,我们的模型优于其他比较模型。所获得的结果表明,高死亡率可能是由糖尿病和高血压等基础疾病引起的,中度肺栓塞Wells评分风险,血小板血细胞计数小于100000(mcl),高血压(HTN),高水平的胆红素,吸烟,和GCS水平在6和9之间。
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.