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Innovative Soft Computing Methodologies for Evaluating Risk Factors of Atherosclerosis

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Abstract

Coronary heart disease (CHD) caused by thickening of inside walls of the arteries known as atherosclerosis is responsible for large number of deaths world-wide. The disease progression is slow, asymptomatic and may lead to sudden cardiac arrest, stroke or myocardial infraction. The observations on patients are available as data in different formats. The attributes in the medical data sets include demographic details, medical history and laboratory examinations having both categorical and/or integer and real types. The biomedical signals and medical images are an important source for identifying the markers of the disease. The demographic details, medical history and laboratory data are the most commonly included in electronic patient records as they are easily obtainable. On the other hand, biomedical signals and medical images are patient specific available in digital format requiring special efforts for acquiring and processing to quantify the disease risk. The genome analysis is fast emerging as an important source for identifying bio-markers of the disease. It is found to be extremely useful in predicting the medical condition of the patient and thus aiding disease prevention. In this chapter we utilize the demographic and laboratory pertaining to the individuals to identify the risk factors of the CHD. We present innovative soft computing methods for processing the medical data for evaluating the risk factors of atherosclerosis and compare their performances with other state-of-the-art machine learning techniques.

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Acknowledgments

This research is supported by the Foundation for Scientific Research and Technological Innovation (FSRTI)—A Constituent Division of Sri Vadrevu Seshagiri Rao Memorial Charitable Trust, Hyderabad-500035, India.

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Naresh Kumar, M., Sree Hari Rao, V. (2013). Innovative Soft Computing Methodologies for Evaluating Risk Factors of Atherosclerosis. In: Furht, B., Agarwal, A. (eds) Handbook of Medical and Healthcare Technologies. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8495-0_6

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