Heart Disease Prediction (Empirically) Using Logistic Regression Analysis by Using SAS Software

Abstract

The main objective of the present study is to investigate factors that contribute significantly to enhancing the risk of heart disease as well as accurately predict the overall risk. The dependent variable of the study is to diagnosis whether the patient has the disease or does not have the disease. Logistic regression analysis is applied for exploring the factors affecting the disease. The early prediction of cardiac diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. This is a technique of using historical information on a certain attribute or event to identify patterns which will assist in predicting a future value of the same with a certain probability attached to it. Its application is invaluable in the field of medical sciences. This paper presents the steps involved in developing a Logistic Regression model based on patient’s heart disease risk. The power of SAS in analyzing data patterns and developing such models is also demonstrated where appropriate and relevant portions of SAS code are included where ever possible.