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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.