College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia.
J Healthc Eng. 2022 Apr 25;2022:4584965. doi: 10.1155/2022/4584965. eCollection 2022.
SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.
SARS-CoV-2 是一种最近发现的病毒，对全球健康构成了紧迫威胁。这种病毒引起的疾病称为 COVID-19。不同国家的死亡人数仍在上升，导致持续的社会隔离和封锁。不同年龄的患者都容易患重病，特别是那些被送入 ICU 的患者。基于医疗数据模式的机器学习 (ML) 预测模型是疾病预测等领域的一个新兴话题。结合多个变量或特征来估计人们感染风险或感染不良后果的预测模型，可以帮助医务人员治疗患者，特别是那些发生器官衰竭（如肝功能衰竭）的患者。在本文中，我们提出了一种称为 COVID-19 ICU 患者肝损伤检测模型（DMLD）的模型，用于预测 COVID-19 ICU 患者的肝损伤风险。DMLD 模型应用机器学习算法，根据患者数据评估肝功能衰竭的风险。为了评估 DMLD 模型，我们对收集的数据进行了预处理，并将其用作几个分类器的输入。我们测试了 SVM、决策树 (DT)、朴素贝叶斯 (NB)、KNN 和 ANN 分类器的性能。SVM 和 DT 在基于实验室检测预测疾病严重程度方面表现最好。
