Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques. (17th May 2022)

• Record Type: Journal Article
• Authors: Mustafa A. Mohammad, Rami; Aljabri, Malak; Aboulnour, Menna; Mirza, Samiha; Alshobaiki, Ahmad
• Other Names: Ramachandran Manikandan Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2022(2022)
• Publication Date: 2022-05-17
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2022/3783058
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : The COVID-19 pandemic has greatly affected populations worldwide and has posed a significant challenge to medical systems. With the constant increase in the number of severe COVID-19 infections, an essential area of research has been directed towards predicting the mortality rate of these patients, in order to make informed medical decisions about the necessary healthcare priorities. Although a large amount of research has attempted to predict the mortality rate of COVID-19 patients, the association between the mortality rate of COVID-19 patients and their underlying health conditions has been given significantly less attention. Meanwhile, patients with underlying conditions often face a worse COVID-19 prognosis. Therefore, the goal of this study was to classify the mortality rate of patients diagnosed with COVID-19, who also suffer from underlying health conditions or comorbidities. To achieve our goal, we applied machine learning (ML) models on a new publicly available dataset, not investigated by any existing literature. The dataset provides detailed information on 582 COVID-19 patients and facilitates a robust forecasting model of the mortality rate. The dataset was analysed using seven ML classifiers, namely, Bagging, J48, logistic regression (LR), random forest (RF), support vector machine (SVM), naïve Bayes (NB), and threshold selector. A comparative analysis was performed across the seven ML techniques, and their performance was assessed based on evaluation parameters including classification accuracy, true-positive rate, and false-positive rate. The best performance was demonstrated by the Bagging algorithm with an accuracy of 83.55% when using all the dataset features. The findings are intended to assist researchers and physicians in the early identification of at-risk COVID-19 patients and to make the appropriate intensive care decisions.