Using Machine Learning to Unveil Demographic and Clinical Features of COVID-19 Symptomatic and Asymptomatic Patients

Abstract

Background: Demographic and clinical features of COVID-19 patients are critical components in shaping their symptomatic status. However, the relationship between patients' symptomatic status and their features are typically complicated and nonlinear.Methods: We explored important features that drive the symptomatic status of COVID-19 patients and reveal their interactions with other relevant factors. We used an extensive multi-algorithm machine learning (ML) pipeline and 68 demographic and clinical features to fit a predictive model to 3,995 patients in the State of Kuwait between February and June 2020. Our ML pipeline comprised five algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting (GBM), and extreme gradient boosting (XGM).Results: SVM outperformed all algorithms (AUC = 0.77 and accuracy = 70.01%), while logistic regression had the lowest predictive power (AUC = 0.65 and accuracy = 66.14%). Our ML model identified C-reactive, respiratory rate, transmission dynamics, and other demographics as the most important predictors of COVID-19 symptomatic patients. While, only demographic features were important predictors for asymptomatic patients. However, our ML model further revealed that the non-linear relationships between impaired renal function, other clinical biomarkers and demographic features were critical in shaping the risk of being symptomatic patient. Conclusions: We demonstrated remarkable predictive performance of our ML model over traditional statistical methods in identifying important clinical and demographic features of symptomatic vs. asymptomatic. Further application of our ML pipeline in the COVID-19 case definition and guiding pharmaceutical and none-pharmaceutical interventions will help reduce the public health and economic implications of this devastating virus on local and global scales.

Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data

“…Machine learning algorithms can explore non-linear and complex relationships by considering the interaction between both biological and psycho-socio-cultural factors together; however, these methods are still underused in the medical field, and few predictive models have been tailored to each sex. 17–19 Therefore, we examined sex-related and gender-related factors associated with SARS-CoV-2 test positivity and COVID-19 hospitalisation in the UK Biobank (UKB) cohort and developed sex-stratified predictive models using machine learning methods.…”

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

“…Various statistical methods can contribute to evaluating the association between independent variables and the dependent variables (leading to sorting the priority of influential variables and eliminating the irrelevant ones), including H-statistics [71], Pearson's correlation analysis [72], chi-square [73], T-test [74], U-test [51], univariate logistic regression [75], etc. While statistical methods can indicate the overall interaction strength of each feature with the other features, they do not convey what the interactions look like.…”