Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data Mining
Introduction:COVID-19 has placed immense burdens on healthcare systems and medical staff. To avoid spread, the statistician’s role and the use of appropriate predictive models -prediction of survivors versus non-survivors- is highly relevant. This study aimed to apply a model which avoids overfittin...
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Mashhad University of Medical Sciences
2023-01-01
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| Series: | Patient Safety and Quality Improvement Journal |
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| Online Access: | https://psj.mums.ac.ir/article_22058_9896fceae375f67ab557abee509bcab5.pdf |
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| author | Nasrin Talkhi Nooshin Akbari sharak Zahra Pasdar Maryam Salari Seyed Masoud Sadati Mohammad Taghi Shakeri |
| author_facet | Nasrin Talkhi Nooshin Akbari sharak Zahra Pasdar Maryam Salari Seyed Masoud Sadati Mohammad Taghi Shakeri |
| author_sort | Nasrin Talkhi |
| collection | DOAJ |
| description | Introduction:COVID-19 has placed immense burdens on healthcare systems and medical staff. To avoid spread, the statistician’s role and the use of appropriate predictive models -prediction of survivors versus non-survivors- is highly relevant. This study aimed to apply a model which avoids overfitting and selection bias towards selecting predictors to predict COVID-19 mortality. Materials and Methods: The Conditional Inference Tree (CIT) model was used. Data from 59,564 hospitalized individuals with positive polymerase chain reaction (PCR) test results were collected from February 20, 2020, to September 12, 2021, in the Khorasan Razavi province, Iran. Results: The sensitivity and specificity of the model were 88.7% and 88.1%, respectively, the accuracy was 88.2%, and the area under the curve (AUC) was 73.0% on test data. Therefore, the model had considerable accuracy in prediction. The potential predictors involved in predicting survivors versus non-survivors were intubation, age, PO2 level, decreased consciousness level, presence of distress, anorexia, drug use, and kidney diseases. Conclusion:According to the findings, the CIT model showed high accuracy by avoiding overfitting and selection bias toward selecting predictors. Thus, the results of this study and the efforts of healthcare systems to stop the spread of this pandemic prove helpful. |
| format | Article |
| id | doaj-art-1b04acaa467d4118997cc7cc918af99a |
| institution | Kabale University |
| issn | 2345-4482 2345-4490 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Mashhad University of Medical Sciences |
| record_format | Article |
| series | Patient Safety and Quality Improvement Journal |
| spelling | doaj-art-1b04acaa467d4118997cc7cc918af99a2025-08-20T03:58:43ZengMashhad University of Medical SciencesPatient Safety and Quality Improvement Journal2345-44822345-44902023-01-01111132110.22038/psj.2023.70741.139022058Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data MiningNasrin Talkhi0Nooshin Akbari sharak1Zahra Pasdar2Maryam Salari3Seyed Masoud Sadati4Mohammad Taghi Shakeri5Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen.Assistant Professor in Biostatistics, Expert Management and Information Technology, Mashhad University of Medical Sciences, Mashhad, Iran.Center of Statistics and Information Technology Management, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.Introduction:COVID-19 has placed immense burdens on healthcare systems and medical staff. To avoid spread, the statistician’s role and the use of appropriate predictive models -prediction of survivors versus non-survivors- is highly relevant. This study aimed to apply a model which avoids overfitting and selection bias towards selecting predictors to predict COVID-19 mortality. Materials and Methods: The Conditional Inference Tree (CIT) model was used. Data from 59,564 hospitalized individuals with positive polymerase chain reaction (PCR) test results were collected from February 20, 2020, to September 12, 2021, in the Khorasan Razavi province, Iran. Results: The sensitivity and specificity of the model were 88.7% and 88.1%, respectively, the accuracy was 88.2%, and the area under the curve (AUC) was 73.0% on test data. Therefore, the model had considerable accuracy in prediction. The potential predictors involved in predicting survivors versus non-survivors were intubation, age, PO2 level, decreased consciousness level, presence of distress, anorexia, drug use, and kidney diseases. Conclusion:According to the findings, the CIT model showed high accuracy by avoiding overfitting and selection bias toward selecting predictors. Thus, the results of this study and the efforts of healthcare systems to stop the spread of this pandemic prove helpful.https://psj.mums.ac.ir/article_22058_9896fceae375f67ab557abee509bcab5.pdfconditional inference treecovid-19data miningdecision treesmachine learning |
| spellingShingle | Nasrin Talkhi Nooshin Akbari sharak Zahra Pasdar Maryam Salari Seyed Masoud Sadati Mohammad Taghi Shakeri Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data Mining Patient Safety and Quality Improvement Journal conditional inference tree covid-19 data mining decision trees machine learning |
| title | Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data Mining |
| title_full | Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data Mining |
| title_fullStr | Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data Mining |
| title_full_unstemmed | Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data Mining |
| title_short | Potential Pathological, Clinical, and Symptomatic Findings of COVID-19 to Predict Mortality in Positive PCR Individuals Using Data Mining |
| title_sort | potential pathological clinical and symptomatic findings of covid 19 to predict mortality in positive pcr individuals using data mining |
| topic | conditional inference tree covid-19 data mining decision trees machine learning |
| url | https://psj.mums.ac.ir/article_22058_9896fceae375f67ab557abee509bcab5.pdf |
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