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Moulaei, khadijeh, Ghasemian, Fahimeh, Bahaadinbeigy, Kambiz, Ershad sarbi, Roghayeh, Mohamadi Taghiabad, Zahra. (1400). Predicting Mortality of COVID-19 Patients based on Data Mining Techniques. سامانه مدیریت نشریات علمی, 11(5), 653-662. doi: 10.31661/jbpe.v0i0.2104-1300
khadijeh Moulaei; Fahimeh Ghasemian; Kambiz Bahaadinbeigy; Roghayeh Ershad sarbi; Zahra Mohamadi Taghiabad. "Predicting Mortality of COVID-19 Patients based on Data Mining Techniques". سامانه مدیریت نشریات علمی, 11, 5, 1400, 653-662. doi: 10.31661/jbpe.v0i0.2104-1300
Moulaei, khadijeh, Ghasemian, Fahimeh, Bahaadinbeigy, Kambiz, Ershad sarbi, Roghayeh, Mohamadi Taghiabad, Zahra. (1400). 'Predicting Mortality of COVID-19 Patients based on Data Mining Techniques', سامانه مدیریت نشریات علمی, 11(5), pp. 653-662. doi: 10.31661/jbpe.v0i0.2104-1300
Moulaei, khadijeh, Ghasemian, Fahimeh, Bahaadinbeigy, Kambiz, Ershad sarbi, Roghayeh, Mohamadi Taghiabad, Zahra. Predicting Mortality of COVID-19 Patients based on Data Mining Techniques. سامانه مدیریت نشریات علمی, 1400; 11(5): 653-662. doi: 10.31661/jbpe.v0i0.2104-1300

Predicting Mortality of COVID-19 Patients based on Data Mining Techniques

Journal of Biomedical Physics and Engineering
مقاله 11، دوره 11، شماره 5، آذر و دی 2021، صفحه 653-662    اصل مقاله (770.15 K)
نوع مقاله: Technical Note
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2104-1300
نویسندگان
khadijeh Moulaei1؛ Fahimeh Ghasemian2؛ Kambiz Bahaadinbeigy* 3؛ Roghayeh Ershad sarbi4؛ Zahra Mohamadi Taghiabad5
1PhD Candidate, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
2PhD, Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University Kerman, Kerman, Iran
3MD, PhD, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
4PhD, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
5MSc, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
چکیده
If Coronavirus (COVID-19) is not predicted, managed, and controlled timely, the health systems of any country and their people will face serious problems. Predictive models can be helpful in health resource management and prevent outbreak and death caused by COVID-19. The present study aimed at predicting mortality in patients with COVID-19 based on data mining techniques. To do this study, the mortality factors of COVID-19 patients were first identified based on different studies. These factors were confirmed by specialist physicians. Based on the confirmed factors, the data of COVID-19 patients were extracted from 850 medical records. Decision tree (J48), MLP, KNN, random forest, and SVM data mining models were used for prediction. The models were evaluated based on accuracy, precision, specificity, sensitivity, and the ROC curve. According to the results, the most effective factor used to predict the death of COVID-19 patients was dyspnea. Based on ROC (1.000), accuracy (99.23%), precision (99.74%), sensitivity (98.25%) and specificity (99.84%), the random forest was the best model in predicting of mortality than other models. After the random forest, KNN5, MLP, and J48 models were ranked next, respectively. Data analysis of COVID-19 patients can be a suitable and practical tool for predicting the mortality of these patients. Given the sensitivity of medical science concerning maintaining human life and lack of specialized human resources in the health system, using the proposed models can increase the chances of successful treatment, prevent early death and reduce the costs associated with long treatments for patients, hospitals and the insurance industry.
کلیدواژه‌ها
Mortality؛ COVID-19؛ Data Mining؛ Prediction
سایر فایل های مرتبط با مقاله
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