Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets

Aug 5, 2026

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COVID-19 has spread rapidly and continues to challenge global healthcare systems by driving urgent needs for reliable prognostic tools. This retrospective study predicts mortality risk in confirmed COVID-19 patients using machine learning models trained on multiple dataset combinations. Admission features include fever, oxygen saturation, laboratory results, thorax CT findings, and comorbidities. To address class imbalance, Synthetic Minority Oversampling Technique was applied. Gradient boosting achieved 98.4% mortality prediction accuracy using CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

Background

Methodology

Results

Conclusions

Introduction

Open Access Original Article DOI: 10.7759/cureus.50932 Review began 12/13/2023 Review ended 12/16/2023 Published 12/22/2023 © Copyright 2023 Panç et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0. , which permits unrestricted use , distribution , and reproduction in any medium , provided the original author and source are credited. Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets Kemal Panç 1 , Nur Hürsoy 1 , Mustafa Başaran 1 , Mümin Murat Yazici 2 , Esat Kaba 1 , Ercan Nalbant 3 , Hasan Gündoğdu 1 , Enes Gürün 4 1. Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR 2. Emergency Medicine, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR 3. Emergency Medicine, Rize State Hospital, Rize, TUR 4. Radiology, Samsun University, Samsun, TUR Corresponding author: Enes Gürün, [e.grn06@gmail.com](e.grn06@gmail.com) Abstract Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML) . In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98 .4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings. Categories: Radiology Keywords: pulmonary artery diameters, lung parenchyma score, gradient boosting, artificial intelligence, covid-19 Introduction The coronavirus discovered in Wuhan, China, in December 2019, was named COVID-19 by the World Health Organization. Since its emergence, it has rapidly spread worldwide and resulted in a pandemic. As of the time of writing, the number of worldwide cases has surpassed 659 million, and the number of deaths has exceeded 6 .6 million, according to data from the World Health Organization [1] . Symptoms of this ailment include fever, dry cough, muscle pain, anosmia, and gastrointestinal system complaints. The majority of symptoms are mild, however, certain patients may suffer from severe complications, which can result in death. The increasing cases have had a critical impact on healthcare research, leading to the development of new methods to forecast patient prognosis. Predicting the mortality of COVID-19 patients is essential in recognizing individuals at a higher risk of severe illness and providing the appropriate care and interventions. Numerous studies have examined risk factors linked to negative consequences and mortality among COVID-19 patients. A systematic review and meta-analysis scrutinized the risk factors of poor outcomes in hospitalized COVID-19 patients and discovered that advanced age, male sex, obesity, diabetes, and cancer were strongly relat

FAQs

What is the study’s primary goal?

To predict mortality risk in COVID-19 patients using machine learning algorithms trained on different datasets.

Which patient data were used in the models?

Fever, oxygen saturation, laboratory results, thorax CT findings, and comorbid diseases at hospital admission.

Which approach produced the best mortality prediction performance?

A gradient boosting model trained on a dataset combining CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, achieving 98.4% accuracy.

Aug 5, 2026

Upload by Hazel