Modeling Aortic Stenosis from Known Risk Factors Using Parametric Methods

Coronary artery disease (CAD) is the leading cause of heart attacks. Heart attacks (or myocardial infarctions) occur when parts of the heart muscle do not receive enough blood. One of the main factors contributing to this loss of blood flow is aortic stenosis. Aortic stenosis is a narrowing of the valve of the aorta that prevents it from opening fully. This results in a decrease in blood flow both to the heart and the rest of the body. In this study we attempt to model the degree of aortic stenosis a person has based on our dataset of three-hundred and three individuals of various ages and genders with fifty-six total variables taken into consideration. Using ten of these variables – the supposed risk factors for aortic stenosis – we begin by testing for mean differences in males versus females and for the age brackets of below seventy years of age and above seventy years of age for the ten risk factors of DM, HTN, FH, CRF, BP, FBS, LDL, HDL, BUN, and VHD. Using parametric analysis on these variables, we create a model based on these risk factors for the degree of aortic stenosis a person might have. This model, in theory, will report of the degree a person’s aortic stenosis based on a few risk factors instead of the current, more expensive tests