Prediction Model of Adverse Effects on Liver Functions of COVID-19 ICU Patients. (25th April 2022)

• Record Type: Journal Article
• Authors: Mashraqi, Aisha; Halawani, Hanan; Alelyani, Turki; Mashraqi, Mutaib; Makkawi, Mohammed; Alasmari, Sultan; Shaikh, Asadullah; Alshehri, Ahmad
• Other Names: Lakshmanna Kuruva Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2022(2022)
• Publication Date: 2022-04-25
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2022/4584965
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : 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.