en بيماريهاي عفوني وگرمسيري Infectious Diseases and Tropical Medicine پژوهشي Original Article <div style="text-align: justify;"><span style="font-size:14px;"><span style="font-family:Tahoma;"><span style="line-height:2;"><strong>Background and Purpose</strong>: Identifying effective symptoms, demographic information, and underlying diseases to predict COVID-19 mortality is essential. We aimed to study the effective clinical and symptomatic characteristics of COVID-19 mortality in hospitalized patients with positive polymerase chain reaction (PCR) test results.<br> <strong>Materials and Methods</strong>: For this study, we prospectively collected complete data on 26867 hospitalized individuals who tested PCR positive for COVID-19 from February 20, 2020, to September 12, 2021, in the Khorasan Razavi Province, Iran. We analyzed the data using artificial neural networks (ANN) and logistic regression (LR) models.<br> <strong>Results</strong>: The accuracy of the ANN model was higher than the LR (90.27% versus 90.15%). The ten most important predictors that contributed to the prediction of death were decreasing consciousness level, cough, PO2 level, age, chronic kidney disease, fever, headache, smoking status, chronic blood diseases, and diarrhea using the ANN model.<br> <strong>Conclusion</strong>: In conclusion, individuals suffering from chronic diseases such as cancer, kidney and blood diseases, as well as immunodeficiency are at a higher risk of mortality. This important finding can help decision-makers and medical professionals in their efforts to consider these conditions and provide effective preventative measures to reduce the risk of death.</span></span></span></div> Machine learning, SARS-CoV-2, COVID-19 diagnostic testing, Logistic regression, Neural network 39 48 http://jhs.mazums.ac.ir/browse.php?a_code=A-10-952-1&slc_lang=en&sid=1 Nasrin Talkhi نسرین تلخی talkhin961@gmail.com 100319475328460011948 100319475328460011948 No Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. Nooshin Akbari Sharak نوشین اکبری شارک AkbariSN962@mums.ac.ir 100319475328460011949 100319475328460011949 No Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. Razieh Yousefi راضیه یوسفی YousefiR981@mums.ac.ir 100319475328460011950 100319475328460011950 No Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. Maryam Salari مریم سالاری Salarim1@mums.ac.ir 100319475328460011951 100319475328460011951 No Department of Biostatistics, Expert Management and Information Technology, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. Seyed Masoud Sadati سید مسعود ساداتی sadatim1@mums.ac.ir 100319475328460011952 100319475328460011952 No Center of Statistics and Information Technology Management, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran. Mohammad Taghi Shakeri محمدتقی شاکری ShakeriMT@mums.ac.ir 100319475328460011953 100319475328460011953 Yes Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.