Innovative Soft Computing Methodologies for Evaluating Risk Factors of Atherosclerosis

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