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Multidimensional Machine Learning for Assessing Parameters Associated With COVID-19 in Vietnam: Validation Study.

Trong Tue Nguyen1,2, Cam Tu Ho3,4, Huong Thi Thu Bui5

  • 1Medical Laboratory Department, Hanoi Medical University, Hanoi, Vietnam.

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Machine learning models identified key factors predicting COVID-19 severity. Age, chest x-ray scores, and neutrophil levels were strongly associated with disease progression, aiding clinical management.

Keywords:
C-reactive proteinCOVID-19agealbuminhierarchical cluster analysismildmoderatemultidimensional analysispercentage and quantity of neutrophilsratio of lymphocytesregression analysisscoring index of chest x-raysevere

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Epidemiology

Background:

  • Machine learning (ML) utilizes artificial intelligence algorithms to analyze large datasets, identify patterns, and perform autonomous tasks.
  • Emerging diseases like COVID-19 generate substantial data, making ML a valuable tool for analysis and prediction.
  • Quantifying and modeling relevant parameters is crucial for understanding disease dynamics and severity.

Purpose of the Study:

  • To determine the preclinical characteristics of COVID-19.
  • To establish cumulative cutoff values and risk ratios (RRs) for disease severity.
  • To identify factors associated with COVID-19 severity using unidimensional and multidimensional analyses in 2173 SARS-CoV-2 patients.

Main Methods:

  • Analysis of 2173 patients categorized into mild, moderate, and severe COVID-19 groups.
  • Utilized correlation tests, relative risk, and RR to identify significant parameters.
  • Employed hierarchical cluster analysis, k-means, and network analysis for parameter classification and visualization.

Main Results:

  • COVID-19 severity correlated significantly with age, chest x-ray scoring index, neutrophil percentage and quantity, C-reactive protein, and lymphocyte ratio.
  • Albumin showed a protective effect (negative correlation) in moderate-severe cases.
  • Network analysis revealed ferritin and age as primary correlates of severity in mild-moderate cases, while ferritin, fibrinogen, and albumin were key in moderate-severe cases.

Conclusions:

  • This multidimensional ML study identified key clinical markers for COVID-19 severity in a Vietnamese population.
  • Findings provide potential reference markers for improved surveillance and diagnostic management of COVID-19.
  • The study highlights the utility of machine learning in dissecting complex disease patterns.