Evaluating of Physiological Chemical Levels in Blood to Assess the Risk of Morbidity and Mortality of Ischemic Cardiovascular Disease

In this study, a multiple linear regression model to evaluate the risk of morbidity and mortality of ischemic cardiovascular disease is demonstrated. In this model, predictor variables are selected from physiological chemicals in a blood test of the subjects. Meanwhile, the calculated risk score is selected as a response variable. Four major latent variables including hepatic, nephric, metabolic, and BMI (Body Mass Index) are revealed by performing statistical and principal component analysis for the collected survey data. The analyzed result also shows that the cardiac disorder is correlated with symptoms of abnormal BMI, hepatic disorder, nephric disorder, and metabolic disorder. Thus, the risk of morbidity and mortality of ischemic cardiovascular disease can be assessed from the proposed multiple regression model.