Authors : Fatemeh Rangraz Jeddi, Majid Nikougoftar Nateghm, Gholamabbass Mosavi and Zahra Shahabinia
Abstract: Cardiovascular disease is now a major cause of mortality throughout the world. Ischemic heart disease with an increasing rate and increased mortality is considered as one of the most expensive and controversial topics in the field of healthcare in the country. Several factors are considered as risk factors. This study aimed to assess factors associated with ischemic heart disease using data mining in 2016. This prospective study of the diagnostic value was carried out in Kashan's Shahid Beheshti hospital in 2015-16. The 345 cardiac patients admitted to Kashan’s Shahid Beheshti hospital were selected based on purposive sampling. The data collection tool was a valid and reliable researcher-made questionnaire and checklist. The questionnaire included questions related to lifestyle and eating habits and checklist included questions regarding to the physiological and laboratory parameters. Patient information was collected through interviews. The data was analyzed then using rapidminer data mining Software, Version 5. The results showed that the accuracy of the data mining algorithm in both decision trees and support vector machine were 87.16 and 97.25%, respectively. All algorithms were able to predict factors in this disease with various degrees of accuracy. To examine factors associated with ischemic heart disease, SVM classification model had the least amount of errors and highest accuracy compared to other models.
Fatemeh Rangraz Jeddi, Majid Nikougoftar Nateghm, Gholamabbass Mosavi and Zahra Shahabinia, 2016. Identification of Factors Affecting Ischemic Heart Disease Using Data Mining Algorithms. Asian Journal of Information Technology, 15: 2509-2515.