A Novel Approach for Estimation of Coronary Artery Calcium Score Class Using ANN and Body Mass Index, Age and Gender Data - Yasar, Huseyin; Serhatlioglu, Selami; Kutbay, Ugurhan; Hardalac, Firat (Ieee, 2018)
Cardiovascular diseases group is the one that causes most death in the world. There is a strong association between coronary artery disease and coronary artery calcium score. Therefore; coronary artery calcium score and class are important for the determination of risk of heart attack. In this study, a new automated assessment system is proposed to estimate the Agatston coronary artery calcium score class without need for measurement. In the estimation study performed under two different titles on the basis of three classes and five classes for Agatston coronary artery calcium score; ANN, body mass index, age and gender were used. In the study, the data collected from a total of 260 patients (105 female, 155 male), ages ranging between 29 and 77 years (an average of 45.56 years), were used. As a result of the study, it was seen that a successful estimation rate of 67.69% was reached in estimating the class of Agatston coronary artery calcium score for the patients correctly when an estimation was made with five classes were taken as basis. In the study, a rate of success of 91.15% was achieved in the estimation based on three classes.
4th International Conference on Computer and Technology Applications (ICCTA) -- MAY 03-05, 2018 -- Istanbul, TURKEY
Keywords: coronary artery calcium score; body mass index; age; gender; artificial neural networks (ANN)