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Iranian Journal of Health Sciences، جلد ۱۳، شماره ۲، صفحات ۱۵۵-۱۶۶
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Classification of Survivors and Non-survivors of the Latest Epidemic Using Association Rules Algorithm |
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| چکیده انگلیسی مقاله |
Background and Purpose: Association rule mining can discover hidden patterns and relationships between variables that may not be apparent through other data analysis techniques. We aimed to find practical patterns in COVID-19 data and predict patient survivor status using association rules.
Materials and Methods: In this cross-sectional study, clinical data of 51460 hospitalized patients tested by polymerase chain reaction (PCR) were collected from February 20, 2020, to September 12, 2021, in Khorasan Razavi Province, Iran. An Apriori algorithm was used to extract association rules or patterns in data.
Results: Most participants (51.0%) were male; their Mean±SD age was 54.55±22.15 years. Fever (37%), cough (38.4%), respiratory distress (56%), PO2 level less than 93% (52.9%), muscular pain (19.1%) and decreased consciousness (8.9%) were common symptoms. Based on the association rules, if a patient was older than 75 years, had respiratory distress, reduced consciousness and PO2 level <93%, then this patient is who has died. The PCR test result of a male who used drugs was positive. Vomit and diarrhea lead to positive PCR test results, too. The most common symptom seen in men was respiratory distress, while the most common symptom in women was hypertension. Muscular pain due to COVID-19 is more common in women than men. Furthermore, the accuracy and area under the receiver operating characteristics curve were obtained as 92.28 and 86.80 on the testing dataset, respectively.
Conclusion: Simple methods such as association rules mining and complex methods could be helpful and give valuable results, and predicting death using association rules provides high accuracy. |
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| کلیدواژه‌های انگلیسی مقاله |
Apriori algorithm, Association rules mining, Associative classifiers, Classification-based association rule (CBA) algorithm, SARS-CoV-2 |
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| نویسندگان مقاله |
| Nasrin Talkhi
Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| Nooshin Akbari sharak
Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| Zahra Pasdar
Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, Scotland.
| Maryam Salari
Expert Management and Information Technology, Mashhad University of Medical Sciences, Mashhad, Iran.
| Seyed Masoud Sadati
Center of Statistics and Information Technology Management, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.
| Mohammad Taghi Shakeri
Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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| نشانی اینترنتی |
http://jhs.mazums.ac.ir/browse.php?a_code=A-10-1095-1&slc_lang=en&sid=1 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
بیماریهای عفونی وگرمسیری |
| نوع مقاله منتشر شده |
پژوهشی |
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