Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines

Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines

Background: As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive...

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Bibliographic Details
Main Authors: Creasy, K.T (Author), Feng, J. (Author), Kane, J.P (Author), Malloy, M.J (Author), Nagar, P. (Author), Saharan, S.S (Author), Stock, E.O (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
adult
aged
Article
AUROC
biological marker
CAD
cardiovascular risk
chemokine
Classification
classifier
Confidence interval
controlled study
coronary angiography
coronary artery disease
cytokine
diagnostic test accuracy study
disease classification
disease exacerbation
Distance metrics
downstream processing
early diagnosis
enzyme activation
false positive result
female
human
immune system
interferon
k fold cross validation
k nearest neighbor
k-fold cross validation
k-NN
lymphokine
machine learning
major clinical study
male
ML
Plasma cytokines
prediction
predictive value
priority journal
process optimization
protein blood level
protein function
random forest
Random Forest
receiver operating characteristic
risk assessment
ROSE
sensitivity and specificity
signal transduction
statistical analysis
statistical model
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