Volume 11, Issue 3 (9-2017)                   payavard 2017, 11(3): 332-341 | Back to browse issues page

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Mahmoodi S A, Mirzaie K, Mahmoodi S M. Determining the Effective Factors in the Incidence of Gastric Cancer by Using Data Mining Approach. payavard. 2017; 11 (3) :332-341
URL: http://payavard.tums.ac.ir/article-1-6295-en.html
1- Master of Science in Computer Engineering, Computer Engineering Department, School of Engineering, Islamic Azad University, Yazd Branch, Yazd, Iran , sa_mahmoodi_85@yahoo.com
2- Assistant Professor, Computer Engineering Department, School of Engineering, Islamic Azad University, Maybod Branch, Maybod, Iran
3- Assistant Professor, Oral Pathology Department, School of Dentistry, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Abstract:   (1553 Views)
Background and Aim: Gastric cancer is the second leading cause of cancer death in the world. Due to the prevalence of the disease and the high mortality rate of gastric cancer in Iran, the factors affecting the development of this disease should be taken into account. In this research, two data mining techniques such as Apriori and ID3 algorithm were used in order to investigate the effective factors in gastric cancer.
Materials and Methods: Data sets in this study were collected among 490 patients including 220 patients with gastric cancer and 270 healthy samples referred to Imam Reza hospital in Tabriz. The best rules related to this data set were extracted through Apriori algorithm and implementing it in MATLAB. ID3 algorithm was also used to investigate these factors.
Results: The results showed that having a history of gastro esophageal reflux has the greatest impact on the incidence of this disease. Some rules extracted through Apriori algorithm can be a model to predict patient status and the incidence of the disease and investigate factors affecting the disease. The prediction accuracy achieved through ID3 algorithm is 85.56 which was a very good result in the prediction of gastric cancer.
Conclusion: Using data mining, especially in medical data, is very useful due to the large volume of data and unknown relationships between systemic, personal, and Behavioral Features of patients. The results of this study could help physicians to identify the contributing factors in incidence of the disease and predict the incidence of the disease.
Full-Text [PDF 526 kb]   (958 Downloads)    
Type of Study: Research | Subject: Health Information Technology
ePublished: 2017/10/14

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