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

Amin Mansoori1,2,3, Nafiseh Hosseini1,4, Hamideh Ghazizadeh1,5

  • 1International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.

BMC Infectious Diseases
|December 22, 2023
PubMed
Summary

Predicting COVID-19 infection is possible using key demographic, biochemical, and hematological factors. Creatine phosphokinase (CPK), body mass index (BMI), and age are among the critical indicators identified for patient classification.

Keywords:
BiochemicalCOVID-19Data miningDecision treesHematologicSARS-COV-2

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Area of Science:

  • Medical Informatics
  • Epidemiology
  • Biochemistry

Background:

  • Coronavirus disease (COVID-19) is a rapidly spreading infectious disease with significant public health implications.
  • Identifying critical factors for COVID-19 infection aids healthcare professionals in patient management.
  • Predictive modeling can assist in distinguishing between infected and non-infected individuals.

Purpose of the Study:

  • To identify critical demographic, biochemical, and hematological features associated with COVID-19 infection.
  • To evaluate the effectiveness of machine learning models in predicting COVID-19 status.
  • To determine key predictors for classifying patients with and without COVID-19.

Main Methods:

  • Utilized a dataset of 13,170 participants aged 35-65 years.
  • Employed Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) algorithms.
  • Developed three models analyzing biochemical features, hematological features, and a combination of both.

Main Results:

  • Model I (biochemical): Identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age as predictors.
  • Model II (hematological): Identified BMI, sex, mean platelet volume (MPV), and age as predictors.
  • Model III (combined): Identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as significant predictors.

Conclusions:

  • Bootstrap Forest (BF), Decision Tree (DT), and Logistic Regression (LR) models effectively predict and classify COVID-19 status.
  • Key predictors for COVID-19 include CPK, BUN, BMI, MPV, FBG, sex, Cr, and age.
  • These identified factors demonstrate a strong association with COVID-19 infection, aiding in patient classification.