{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T10:29:59Z","timestamp":1785148199015,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Death due to covid\u201019 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID\u201019. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID\u201019.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID\u201019, chi\u2010square test was performed with a significance level of 0.05. To compare DT, SVM and AdaBoost models for predicting death due to COVID\u201019 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over\u2010sampling examples, rpart, rpartplot packages was done.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Out of the total of 23,054 patients studied, 10,935 cases (46.5%) were women, and 12,569 cases (53.5%) were men. Additionally, the mean age of the patients was 54.9\u2009\u00b1\u200921.0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid\u201019 patients showed (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). The results showed that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic curve were respectively 0.60, 0.68, 0.71, and 0.75 in the DT model, 0.54, 0.62, 0.63, and 0.71 in the SVM model, and 0.59, 0.65, 0.69 and 0.74 in the AdaBoost model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The results showed that DT had a high predictive power compared to other data mining models. Therefore, it is suggested to researchers in different fields to use DT to predict the studied variables. Also, it is suggested to use other approaches such as random forest or XGBoost to improve the accuracy in future studies.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hsr2.2266","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T07:44:41Z","timestamp":1721893481000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The most important variables associated with death due to COVID\u201019 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross\u2010sectional study"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3812-7860","authenticated-orcid":false,"given":"Bita Shokri","family":"gharehhasani","sequence":"first","affiliation":[{"name":"Taleghani Hospital Kermanshah University of Medical Sciences  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mansour","family":"Rezaei","sequence":"additional","affiliation":[{"name":"Social Development and Health Promotion Research Center Kermanshah University of Medical Sciences  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Naghipour","sequence":"additional","affiliation":[{"name":"Clinical Research Development Center, Imam Khomeini and Dr.Mohammad Kermanshahi and Farabi Hospitals Kermanshah University of Medical Sciences  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nazanine","family":"Sayad","sequence":"additional","affiliation":[{"name":"Kermanshah University of Medical Sciences  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shayan","family":"Mostafaei","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics Karolinska Institute  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehsan","family":"Alimohammadi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Kermanshah University of Medical Sciences  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/nop2.1653"},{"issue":"4","key":"e_1_2_10_3_1","article-title":"The effects of blood group types on the mortality and survival of patients with COVID\u201019 infection","volume":"27","author":"Naghibifar Z","year":"2023","journal-title":"J Kerman Univer Med Sci"},{"issue":"1","key":"e_1_2_10_4_1","article-title":"Evaluating the motivation to accept\/not accept the COVID\u201019 vaccination and its related factors in health care workers: a cross\u2010sectional study","volume":"28","author":"Janbakhsh A","year":"2024","journal-title":"J Kermanshah Univer Med Sci"},{"key":"e_1_2_10_5_1","unstructured":"SalarianS KarrabiK AsgariS BehnazF. 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