Novel Approaches for Predicting Risk Factors of Atherosclerosis

Coronary heart disease (CHD) caused by hardening of artery walls due to cholesterol known as atherosclerosis is responsible for large number of deaths worldwide. The disease progression is slow, asymptomatic, and may lead to sudden cardiac arrest, stroke, or myocardial infraction. Presently, imaging...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2013-01, Vol.17 (1), p.183-189
Main Authors: Rao, V. S. H., Kumar, M. N.
Format: Article
Language:English
Subjects:
Accuracy
Atherosclerosis
Atherosclerosis - diagnosis
Cardiovascular diseases
classification
Computer Simulation
Databases, Factual
Decision Trees
feature selection
Heart
Humans
Imaging
imputation
Male
Middle Aged
Models, Statistical
particle swarm optimization (PSO)
prediction
Reproducibility of Results
Risk Factors
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Summary:Coronary heart disease (CHD) caused by hardening of artery walls due to cholesterol known as atherosclerosis is responsible for large number of deaths worldwide. The disease progression is slow, asymptomatic, and may lead to sudden cardiac arrest, stroke, or myocardial infraction. Presently, imaging techniques are being employed to understand the molecular and metabolic activity of atherosclerotic plaques to estimate the risk. Though imaging methods are able to provide some information on plaque metabolism, they lack the required resolution and sensitivity for detection. In this paper, we consider the clinical observations and habits of individuals for predicting the risk factors of CHD. The identification of risk factors helps in stratifying patients for further intensive tests such as nuclear imaging or coronary angiography. We present a novel approach for predicting the risk factors of atherosclerosis with an in-built imputation algorithm and particle swarm optimization (PSO). We compare the performance of our methodology with other machine-learning techniques on STULONG dataset which is based on longitudinal study of middle-aged individuals lasting for 20 years. Our methodology powered by PSO search has identified physical inactivity as one of the risk factors for the onset of atherosclerosis in addition to other already known factors. The decision rules extracted by our methodology are able to predict the risk factors with an accuracy of 99.73% which are higher than the accuracies obtained by the application of the state-of-the-art machine-learning techniques presently being employed in the identification of atherosclerosis risk studies.
ISSN:2168-2194
2168-2208
DOI:10.1109/TITB.2012.2227271