Volume 18, Issue 3 (Vol.18, No.3, Autumn 2022)
irje 2022, 18(3): 244-254 |
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Talkhi N, Akbari sharak N, Rajabzadeh Z, Salari M, Sadati S M, Shakeri M T. Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach. irje 2022; 18 (3) :244-254
URL: http://irje.tums.ac.ir/article-1-7111-en.html
URL: http://irje.tums.ac.ir/article-1-7111-en.html
Nasrin Talkhi1 
, Nooshin Akbari sharak1 , Zahra Rajabzadeh1 , Maryam Salari2 , Seyed Masoud Sadati3 , Mohammad Taghi Shakeri * 4
, Nooshin Akbari sharak1 , Zahra Rajabzadeh1 , Maryam Salari2 , Seyed Masoud Sadati3 , Mohammad Taghi Shakeri * 4
1- MSc of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
2- Assistant Professor in Biostatistics, Expert Management and Information Technology, Mashhad University of Medical Sciences, Mashhad, Iran
3- MSc of IT management, Center of Statistics and Information Technology Management, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
4- Professor in Biostatistics, Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran ,ShakeriMT@mums.ac.ir
2- Assistant Professor in Biostatistics, Expert Management and Information Technology, Mashhad University of Medical Sciences, Mashhad, Iran
3- MSc of IT management, Center of Statistics and Information Technology Management, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
4- Professor in Biostatistics, Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran ,
Abstract: (761 Views)
Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province.
Methods: This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19.
Results: Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively.
Conclusion: Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.
Methods: This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19.
Results: Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively.
Conclusion: Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.
Type of Study: Research |
Subject:
Epidemiology
Received: 2022/07/3 | Accepted: 2023/01/21 | Published: 2022/12/1
Received: 2022/07/3 | Accepted: 2023/01/21 | Published: 2022/12/1
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