Write your message
Volume 3, Issue 4 (Fall 2018)                   jogcr 2018, 3(4): 149-155 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Kafaee Ghaeini M, Amin-Naseri M R, Aghahoseini M. Prediction of Clinical Pregnancy Occurrence After ICSI using Decision Tree and Support Vector Machine Methods. jogcr. 2018; 3 (4) :149-155
URL: http://jogcr.com/article-1-202-en.html
1- MSc student of Industrial Engineering-System Management, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
2- Professor, Industrial and Systems Engineering,Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran , amin_nas@modares.ac.ir
3- Professor of Medical School, Tehran University of Medical Sciences, Tehran, Iran
Abstract:   (167 Views)

Background & Objective: Studies have shown that despite the numerous research carried out regarding infertility treatment, there is still a long way to treat this disease satisfactorily. Spending a lot of time and money on infertility treatments proves the necessity of designing a model which could predict the result of treatment methods with an acceptable accuracy; a model that could help physicians to get rid of trial and error for treatment methods which should step by step be applied on an infertile couple. Intracytoplasmic Sperm Injection (ICSI) is one of the assisted reproductive techniques. Statistics have indicated that the probability of pregnancy occurrence is only about 30% using this method. In this paper, a model which could predict the result of (ICSI) was presented using the decision tree and support vector machine methods.
Materials & Methods: The applied data were collected in seven months from December 2012 to June 2013 by analyzing 251 treatment cycles in Omid Fertility Clinic. Input variables of the model were parameters like couple’s medical records, hormonal tests, the cause of infertility, and the like. The output variable was the occurrence or nonoccurrence of the clinical pregnancy (the pregnancy resulting in the formation of the fetal heart). One of the innovations of this study was that the input variables of the model were only preoperative, while in previous studies, having information about some of the surgery stages, such as quality of the egg and the like, was required to anticipate the result of the surgery.
Results: The obtained accuracy using the decision tree and support vector machine methods were 70.3% and 75.7%, respectively.
Conclusion: The results of the current study demonstrated that the support vector machine method had a better performance compared to the decision tree method. Presented model predicts the occurrence or nonoccurrence of a clinical pregnancy follows (ICSI), with a precision of 75.7%.

Full-Text [PDF 328 kb]   (52 Downloads) |   |   Full-Text (HTML)  (15 Views)  

✅ The results of the current study demonstrated that the support vector machine method had a better performance compared to the decision tree method. Presented model predicts the occurrence or nonoccurrence of a clinical pregnancy follows (ICSI), with a precision of 75.7%.


Systematic Review: Original Research | Subject: General
Received: 2018/05/5 | Accepted: 2019/08/4 | Published: 2018/09/16

References
1. Rostami Dovom, M., Ramezani Tehrani, F., Abedini, M., Amirshekari, G., Hashemi, S., & Noroozzadeh, M. 2014. A population-based study on infertility and its influencing factors in four selected provinces in Iran (2008-2010). Iranian journal of reproductive medicine, 12(8), 561-566.
2. CORANI, G., MAGLI, C., GIUSTI, A., GIANAROLI, L. & GAMBARDELLA, L. 2013. A Bayesian network model for predicting pregnancy after in vitro fertilization. Computers in biology and medicine, 43, 1783-1792. [DOI:10.1016/j.compbiomed.2013.07.035] [PMID] [DOI:10.1016/j.compbiomed.2013.07.035] [PMID]
3. Otani, S., Iwai, T., Nakahata, S., Sakai, C., & Yamashita, M. 2009. Artificial fertilization by intracytoplasmic sperm injection in a teleost fish, the medaka (Oryzias latipes). Biology of reproduction, 80(1), 175-183. [DOI:10.1095/biolreprod.108.069880] [PMID] [DOI:10.1095/biolreprod.108.069880] [PMID]
4. GUH, R.-S., WU, T.-C. J. & WENG, S.-P. 2011. Integrating genetic algorithm and decision tree learning for assistance in predicting in vitro fertilization outcomes. Expert Systems with Applications, 38, 4437-4449. [DOI:10.1016/j.eswa.2010.09.112] [DOI:10.1016/j.eswa.2010.09.112]
5. SOHRABVAND, F., SHARIAT, M., FOTOOHI GHIAM, N. & HASHEMI, M. 2009. The relationship between number of transferred embryos and pregnancy rate in ART cycles. Tehran University Medical Journal, 67(2), 132-136.
6. ORVIETO, R., MELTCER, S., NAHUM, R., RABINSON, J., ANTEBY, E. Y. & ASHKENAZI, J. 2009. The influence of body mass index on in vitro fertilization outcome. International Journal of Gynecology & Obstetrics, 104, 53-55. [DOI:10.1016/j.ijgo.2008.08.012] [PMID] [DOI:10.1016/j.ijgo.2008.08.012] [PMID]
7. PINBORG, A., GAARSLEV, C., HOUGAARD, C., NYBOE ANDERSEN, A., ANDERSEN, P., BOIVIN, J. & SCHMIDT, L. 2011. Influence of female bodyweight on IVF outcome: a longitudinal multicentre cohort study of 487 infertile couples. Reproductive BioMedicine Online, 23, 490-499. [DOI:10.1016/j.rbmo.2011.06.010] [PMID] [DOI:10.1016/j.rbmo.2011.06.010] [PMID]
8. HAGHIGHI, Z., REZAEI, Z. & ES-HAGHI ASHTIANI, S. 2012. Effects of women's body mass index on in vitro fertilization success: a retrospective cohort study. Gynecological Endocrinology, 28, 536-539. [DOI:10.3109/09513590.2011.650657] [PMID] [DOI:10.3109/09513590.2011.650657] [PMID]
9. Siristatidis, C. S., Chrelias, C., Pouliakis, A., Katsimanis, E., & Kassanos, D. 2010. Artificial neural networks in gynaecological diseases: Current and potential future applications. Medical Science Monitor, 16(10), RA231-RA236.
10. Siristatidis, C., Pouliakis, A., Chrelias, C., & Kassanos, D. 2011. Artificial intelligence in IVF: a need. System Biology in Reproductive Medicine, 57(4), 179-185. [DOI:10.3109/19396368.2011.558607] [PMID] [DOI:10.3109/19396368.2011.558607] [PMID]
11. DORMAHAMMADI, S., ALIZADEH, S., ASGHARI, M. & SHAMI, M. 2014. Proposing a prediction model for diagnosing causes of infertility by data mining algorithms. Journal of Health Administration, 57(17), 46-57.
12. MILEWSKA, A.J., JANKOWSKA, D., CWALINA, U., CITKO, D., WIESAK, T., ACACIO, B. & MILEWSKI, R. 2015. Significance of discriminant analysis in prediction of pregnancy in IVF treatment. Studies in Logic, Grammar and Rhetoric, 43, 7-20. [DOI:10.1515/slgr-2015-0038] [DOI:10.1515/slgr-2015-0038]
13. MILEWSKI, R., JANKOWSKA, D., CWALINA, U., MILEWSKA, A.J., CITKO, D., WIESAK, T., MORGAN, A. & WOLCZYNSKI, S. 2016. Application of artificial neural networks and principal component analysis to predict results of infertility treatment using the IVF method. Studies in Logic, Grammar and Rhetoric, 47(1), 33-46. [DOI:10.1515/slgr-2016-0045] [DOI:10.1515/slgr-2016-0045]
14. MILEWSKI, R., KUCZYNSKA, A., STANKIEWICZ, B. & KUCZYNSKI, W. 2017. How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis. Advances in Medical Sciences, 62(1), 202-206. [DOI:10.1016/j.advms.2017.02.001] [PMID]
15. SEPEHRI, M.M., RAHNAMA, P., SHADPOUR, P. & TEIMOURPOUR, B. 2009. A data mining based model for selecting type of treatment for kidney stone patients. Tehran University Medical Journal, 67(6), 421-427.
16. HAN, J., KAMBER, M. & PEI, J. 2011. Data mining: concepts and techniques, Morgan kaufmann.
17. TAN, P.N., STEINBACH, M. & KUMAR, V. 2018. Introduction to data mining, Pearson Education India.
18. VIEIRA, S. M., MENDONCA, L. F., FARINHA, G. J. & SOUSA, J. 2013. Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Applied Soft Computing. [DOI:10.1016/j.asoc.2013.03.021] [DOI:10.1016/j.asoc.2013.03.021]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2019 All Rights Reserved | Journal of Obstetrics, Gynecology and Cancer Research (JOGCR)

Designed & Developed by : Yektaweb | Piblisher: Farname Inc.