Volume 24, Issue 105 (6-2016)                   J Adv Med Biomed Res 2016, 24(105): 97-106 | Back to browse issues page

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Bahrami L, Sadeghi Bigham B, Kamali K. The Effect of some Genetic Mutations on Endometriosis Using Data Mining Techniques. J Adv Med Biomed Res 2016; 24 (105) :97-106
URL: http://journal.zums.ac.ir/article-1-3587-en.html
1- Dept. of Computer, Islamic Azad University, Zanjan Branch, Zanjan, Iran
2- Dept. of Computer, Faculty of Computer, University of Basic Sciences, Zanjan, Iran , b_sadeghi_b@iasbs.ac.ir
3- Dept. of Epidemiology, Metabolic Disease Research Center, Zanjan University of Medical Sciences, Zanjan, Iran
Abstract:   (155760 Views)

Background and Objective: Endometriosis is a prevalent disease in women which may lead to infertility or low fertility. Grasping the genetic grounds for the disease may contribute to its treatment because it is presumed that genetic factors predispose to endometriosis risk factors. 

Materials and Methods: 9 genes involved in endometriosis in patients suffering from endometriosis and also in healthy individuals (total 260 samples) were examined. The data were obtained from Ibn Sina (Jahad Daneshgahi) Research Center of new Technologies in Biological Sciences Institute. The study incorporated standard process Crisp for data mining. Weka data mining and software modeling were implemented with the aid of four algorithms.

Results: Comparison of four algorithms implied prominent accuracy of K-Star model. Meanwhile, filtering, while reducing the percentage of models, presented a positive impact on the MLP model. The lowest percentage pertained to Naïve Bayes.

Conclusion: K-Star model without any filtering proved to have the highest accuracy in the early diagnosis of endometriosis.

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Type of Study: Clinical Trials |
Received: 2016/03/16 | Accepted: 2016/03/16 | Published: 2016/03/16

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