Framingham Risk Score by Data Mining Method

Abstract

There are cleaning, integration, reduction, conversion, algorithm implementation and evaluation stages in data mining meaning finding necessary data from a wide variety of variables and data. It is important to create a data warehouse to realize these steps. Data randomly selected from data warehouse is evaluated with certain algorithms. While deaths resulting from heart diseases in our country are 37% according to 2016 data, 420-440 thousand people are diagnosed as heart patients each year and the number of deaths per year can reach 340 thousand people. These values correspond to approximately three times of Europe. In this study, risk of heart attack is calculated by data mining method by taking advantage of Framingham risk score. In order to determine this risk factor; 10-year risk is calculated by looking at sex, age, total cholesterol, HDL cholesterol, blood pressure, diabetes and smoking. While the effects of the ages for men starts -9 points, ends with +13 points and for women starts -7 points, ends with +16 points. While the effects of the total cholesterol for men starts 0 points, ends with +11 points and for women starts 0 points, ends with +13 points. Total scores are between 0-17 and over in men, and scores between 0-25 and over in women. There are risk values ranging from 1% to 30%.