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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.
Insights
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.
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.
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.
