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Updated: Sep 20, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Predicting the Risk Factors Associated With Severe Outcomes Among COVID-19 Patients-Decision Tree Modeling Approach.

Mahalakshmi Kumaran¹, Truong-Minh Pham², Kaiming Wang^3,4

¹Surveillance and Reporting, Provincial Population and Public Health, Alberta Health Services, Calgary, AB, Canada.

Frontiers in Public Health

|June 6, 2022

Summary

Older age and breathing difficulties are key predictors of severe COVID-19 outcomes. Identifying these high-risk groups, including younger adults with obesity, aids resource allocation for better patient care.

Keywords:

COVID-19 SARS-CoV-2 decision tree modeling machine learning outcome

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Area of Science:

Epidemiology
Medical Informatics
Public Health

Background:

The COVID-19 pandemic caused significant healthcare system strain due to high case numbers and severe outcomes.
A substantial proportion of COVID-19 patients required hospitalization and intensive care unit (ICU) admission.
Identifying predictors of severe COVID-19 outcomes is crucial for effective resource management.

Purpose of the Study:

To identify key predictors of severe outcomes in COVID-19 patients.
To utilize decision tree modeling for analyzing factors associated with hospitalization, ICU admission, and death.
To inform strategies for identifying high-risk populations and optimizing healthcare resource allocation.

Main Methods:

Retrospective analysis of a population-based cohort of 140,182 adult COVID-19 patients.
Data extraction from communicable disease systems and electronic medical records, including demographics, symptoms, and comorbidities.
Application of decision tree modeling, including conditional inference trees and random forests, to identify outcome predictors.

Main Results:

Older age (>71 years) and breathing difficulties were the strongest predictors of severe COVID-19 outcomes, accounting for approximately 50% of severe cases.
Pre-existing conditions like neurological disorders, diabetes, cardiovascular disease, hypertension, and renal disease collectively predicted 29% of outcomes.
In younger adults (18-40 years), obesity emerged as a significant risk factor for adverse outcomes.

Conclusions:

Decision tree modeling effectively identified key factors associated with severe COVID-19 outcomes.
Understanding these predictors enables the identification of high-risk individuals and groups.
This knowledge supports targeted interventions and efficient allocation of healthcare resources during the pandemic.