Association between biochemical and hematologic factors with COVID-19 using data mining methods.

Bibliographic Details
Title:	Association between biochemical and hematologic factors with COVID-19 using data mining methods.
Authors:	Mansoori, Amin^1,2,3 (AUTHOR), Hosseini, Nafiseh^1,4 (AUTHOR), Ghazizadeh, Hamideh^1,5 (AUTHOR), Aghasizadeh, Malihe¹ (AUTHOR), Drroudi, Susan¹ (AUTHOR), Sahranavard, Toktam¹ (AUTHOR), Izadi, Hanie Salmani⁶ (AUTHOR), Amiriani, Amirhossein⁶ (AUTHOR), Farkhani, Ehsan Mosa¹ (AUTHOR), Ferns, Gordon A.⁷ (AUTHOR), Ghayour-Mobarhan, Majid¹ (AUTHOR), Moohebati, Mohsen⁸ (AUTHOR) mouhebatim@mums.ac.ir, Esmaily, Habibollah^3,9 (AUTHOR) esmailyh@mums.ac.ir
Superior Title:	BMC Infectious Diseases. 12/21/2023, Vol. 23 Issue 1, p1-13. 13p.
Subject Terms:	MEDICAL personnel, DATA mining, COVID-19, MEAN platelet volume, *BLOOD urea nitrogen
Abstract:	Background and aim: Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection. Method: A total of 13,170 participants in the age range of 35–65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied. Results: In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors. Conclusion: The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19. [ABSTRACT FROM AUTHOR]
	Copyright of BMC Infectious Diseases is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Academic Search Premier
Full text is not displayed to guests.	Login for full access.

Description
Description not available.