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

L Bahrami, B Sadeghi Bigham
2016 · doi:10.5281/zenodo.3783824 · W6949965097
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This study used data mining algorithms on genetic data from endometriosis patients and healthy individuals to find that the K-Star model achieved the highest accuracy for early diagnosis.

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This study evaluated the utility of data mining techniques for diagnosing endometriosis by analyzing genetic mutations in 260 participants, including both patients and healthy controls. Researchers examined nine specific genes associated with the condition using the CRISP-DM framework and implemented four distinct algorithms via Weka software to assess their predictive performance. The results indicated that the K-Star model achieved the highest accuracy for early diagnosis when no filtering was applied, whereas filtering positively impacted the Multi-Layer Perceptron model but yielded lower overall effectiveness compared to other methods. This paper is centrally about endometriosis — specifically focusing on computational approaches to identify genetic markers for its early detection.

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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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