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Showing 2 results for Apriori Algorithm
Determining the Effective Factors in the Incidence of Gastric Cancer by Using Data Mining Approach
Seyed Abbas Mahmoodi , Kamal Mirzaie, Seyed Mostafa Mahmoodi ,
Volume 11, Issue 3 (9-2017)
Abstract
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.

Identifying Important Factors of Arthroplasty in Patients with Degenerative Knee Osteoarthritis Based on Association Rule Mining Approach
Seyed Mohammad Javad Mortazavi, Fatemeh Torabi Konjin, Behrouz Minaei Bidgoli, Ali Okati,
Volume 13, Issue 3 (9-2019)
Abstract
Background and Aim: Total Knee Arthroplasty (TKA) aims to reduce the pain and improve the quality of life of patients with progressive osteoarthritis. When the indication of patients' disease is established, this type of surgery should be performed as soon as possible because patients' late attendance increases surgical complications. Therefore, identification of factors influencing the choice of this type of treatment approach is of great importance. The purpose of this study is to identify the factors that influence the choice of this treatment approach in patients using the Apriori algorithm in the form of Association Rules.
Materials and Methods: This study is performed on 233 patients referring to Imam Khomeini Hospital in Tehran for a knee replacement surgery; the needed data have been registered at Bone and Joint Reconstruction Research Center. In this study, after the preprocessing stage, the important factors in decision making of knee replacement surgery have been identified by using the Apriori algorithm and by its implementation in the software environment of RStudio. After being extracted, these factors and the relationship among them are given to orthopedic practitioners for confirmation.
Results: In this study, flexion contracture above 20 degrees, deformity (varous-valgus) above 15 degrees, final flexion between 51-75 degrees, and medial cartilage destruction were, respectively, the most important factors in selecting patients for knee replacement therapy.
Conclusion: The results showed that data-mining Algorithms could be used to identify effective factors to select patients for this treatment approach.

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