Evaluating the Application of Machine Learning in Predicting the Mortality of Hospitalized COVID-19 Patients Using the Confusion Matrix and the Matthews Correlation Coefficient

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Salari Maryam; Sadati Seyed Masoud; Sedaghat Alireza; Abbasi Bita; Zamanpour Seyed Amir; Khodashahi Rozita; Davoudi Mostafa

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Journal Paper

Paper Information

Journal: ARCHIVES OF CLINICAL INFECTIOUS DISEASES Year:2025 | Volume:20 | Issue:2 Page(s): 1-11

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Title

Evaluating the Application of Machine Learning in Predicting the Mortality of Hospitalized COVID-19 Patients Using the Confusion Matrix and the Matthews Correlation Coefficient

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Keywords

Machine Learning

COVID-19

Adaptive Boosting

HRCT

Laboratory Tests

Abstract

Background: The COVID-19 pandemic, which occurred between 2019 and 2023, posed a significant threat to global health. Its high transmissibility, the emergence of new variants, and the novel nature of the disease made treatment and control highly challenging.Objectives: This study aimed to develop an algorithm for predicting the mortality of hospitalized COVID-19 patients using machine learning methods. Methods: This cross-sectional study was conducted on 581 hospitalized COVID-19 patients. The approach integrated multi-model features derived from computed tomography (CT) scans and electronic health record (EHR) data. High-resolution computed tomography (HRCT) images were initially processed using the Pulmonary Toolkit package in MATLAB software. Subsequently, the extracted variables were entered into the model as predictive factors, alongside demographic characteristics, underlying conditions, and laboratory results of the patients. The machine learning model was developed using the AdaBoost method by incorporating demographic and laboratory data with HRCT features. Results: In this study, 581 hospitalized COVID-19 patients were included. Among them, 199 (34.25%) patients died, while 382 (65.75%) recovered. According to the machine learning algorithm, the most effective variables for predicting COVID-19 mortality were lymphocyte variables, CRP, age, mean lung density, lung tissue percentage, RBC count, D-dimer levels, and emphysema. The MCC Index in this study was 0.73, and the area under the ROC curve was 0.96. Conclusions: According to our results, the three variables with the greatest impact on predicting mortality in COVID-19 patients were related to HRCT findings, laboratory results, and patient age. Therefore, it is recommended that, given the high cost of HRCT, this diagnostic test should only be performed if other risk factors are identified in laboratory results. If necessary, HRCT should be conducted promptly.

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