The effect of some genetic mutations on endometriosis using data mining techniques

L Bahrami, B Sadeghi Bigham
2016 · doi:10.5281/zenodo.3783823 · W6912324579
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Abstract

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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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. Files 14) The effect of some genetic mutations on endometriosis using data mining techniques.pdf Files (189.1 kB) | Name | Size | Download all | |---|---|---| | md5:0a420a3668cb780365d235f4d9097ac5 | 189.1 kB | Preview Download |

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endometriosis

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