Volume 26, Issue 6 (9-2019)
RJMS 2019, 26(6): 118-126 |
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Hashemi Karouei S F, Abdolmaleki P, Joorsaraei S G. Prediction the success rate of intracytoplasmic sperm injection (ICSI) using artificial neural networks and logistic regression. RJMS 2019; 26 (6) :118-126
URL: http://rjms.iums.ac.ir/article-1-5365-en.html
URL: http://rjms.iums.ac.ir/article-1-5365-en.html
Tarbiat Modares University, Tehran, Iran , parviz@modares.ac.ir
Abstract: (2886 Views)
Background: Intra-cytoplasmic sperm injection (ICSI) in infertile couples has created a great improvement for treatment of these patients. Unfortunately, despite of high cost of doing ICSI the rate of success is not acceptable and failing pregnancy put a heavy anxiety to couples. This study is aimed to make an effort in order to extract the best predictors for predict the success rate of intra cytoplasmic sperm injection and promote accuracy, sensivity and specificity by the use of artificial neural network and logistic regression.
Methods: 345 patients received ICSI treatment and each of which constructed of 54 numerical and nominal records. This database was randomly divided into the estimation (n=276) and validation (n=69) data set. The models were used based on binary logistic regression (BLR) feature selection tools and Levenberg-Marquardth neural network classifier. Finally, models were evaluated using important criteria such as accuracy, sensitivity and specificity.
Results: The best output of the B LR model by using 54 variables revealed accuracy (97%), sensitivity (86%)and specificity (%94). The best output of the LMNN model using Reduced dataset consisted of n=29 with a feature vector side yielded the accuracy (82%) and sensitivity (%92) and specificity (%76).
Conclusion: Our result demonstrated that BLR model outperformed highlighting the great power of BLR in success rate of ICSI prediction while using binary output.
Methods: 345 patients received ICSI treatment and each of which constructed of 54 numerical and nominal records. This database was randomly divided into the estimation (n=276) and validation (n=69) data set. The models were used based on binary logistic regression (BLR) feature selection tools and Levenberg-Marquardth neural network classifier. Finally, models were evaluated using important criteria such as accuracy, sensitivity and specificity.
Results: The best output of the B LR model by using 54 variables revealed accuracy (97%), sensitivity (86%)and specificity (%94). The best output of the LMNN model using Reduced dataset consisted of n=29 with a feature vector side yielded the accuracy (82%) and sensitivity (%92) and specificity (%76).
Conclusion: Our result demonstrated that BLR model outperformed highlighting the great power of BLR in success rate of ICSI prediction while using binary output.
Type of Study: Research |
Subject:
Gynecology
References
1. 1. HashemianNaeini E, AkbariAsbagh F, ZareNiyestanak M, Mehrsay A, Foroozandeh E, Shokoohi M, et al. Factors affecting pregnancy and abortion rates following intra cytoplasmic sperm injection in the obstructive and non-obstructive azoospermicmen. Med Sci; 2012.22(3):211-215.
2. 2. Speroff L, Fritz M. Assisted reproductive technologies. In: Weinberg RW, Editor. Clinical Gynecologic Endocrinology and Infertility .7th ed. Philadelphia: Lippincot Williams and Wilkins; 2005:1215-38.
3. 3. Manoli V, Dandekar S, Desai S, Mangoli R. The outcome of ART in males with impaired spermatogenesis. J Hum Reprod Sci; 2008.1:73-76.
4. 4. Hollingsworth B, Harris A, Mortimer D. The cost effectiveness of intracytoplasmic sperm injection (ICSI). J Assist Reprod Genet; 2007.24:571-77.
5. 5. Wald M, Speaker AET, Sandlow J, Van-Vorhis B, Syrop CH, Niederberger CS. Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa. Reproduct Biomed Online; 2005.11(3):325-331.
6. 6. Tournaye H, DevroeyP, Liu J, Nagy Z, Lissens W, Van Steirteghem A. [Microsurgical epididimal sperm aspiration and intracytoplasmic sperm injection:a new effective approach to infertility as a result of congenital bilateral absence of the vas deferens]. Fertile Steril; 1994.61:1045-51.
7. 7. Zarinara A, Zeraati H, Kamali K, Mohammad K, Shahnazari P, Akhondi MM. [Models predicting success of infertility treatment: A systematic review]. J Reprod Infertil; 2016.17(2):66-81.
8. 8. Durairaj M, Thamilselvan P. [Applications of artificial neural network for IVF data analysis and prediction]. J Engineer Comput Appl Sci; 2013.2(9).
9. 9. Durairaj M, Meena K. [Application of artificial neural network for predicting fertilization potential of frozen spermatozoa of cattle and buffalo]. Int J Comput Sci Syst Analyz; 2008:1-10.
10. 10. Larsson H, Rodrigues M. Can we use in vitro fertilization tests to predict semen fertility? Animal Reprod Sci; 2000. 61:327-336.
11. 11. Malinowski P, Milewski R, Ziniewics P, Milewska AJ, Czerniecki J, Wołczyński S . [The use of data mining methods to predict the result of infertility treatment using the IVF ET method]. Stud Log Grammar Rhetoric; 2014.39(52):67-74.
12. 12. Milewska AJ, Jankowska D, Cwalina U, WięsakT, Citko D, Morgan A, et al. [Analyzing outcomes of intrauterine insemination treatment by application of cluster analysis or kohonen neural network] .Studies In Logic, Grammar And Rhetoric. Log Stat Comput Methods Med; 2013.35(48):7-25.
13. 13. Hayatshahi SHS, Abdolmaleki P, Safarian Sh, Khajeh Kh. [Non –linear quantitative structure – activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase]. Biochem Biophysic Res Commun; 2005.1137-1142.
14. 14. Kaufman SJ, Eastaugh L, Snowden S, Swye Sw, Sharma V. [The application of neural networks in predicting the outcome of in-vitro fertilization]. Hum Reprod; 1997.12(7):1454-1457.
15. 15. Farsi MM, Jorsaraei A, Hajiahmadi M, Esmaelzadeh S. [Role of embryo morphology in intracytoplasmic sperm injection cycles for prediction of pregnancy]. Iran J Reprod Med; 2007. 5(1):23-27.
16. 16. Milewski R, Milewska AJ, Więsak T, Morgan A. [Comparison of artificial neural network and logistic regression analyze in pregnancy prediction using the in vitro fertilization treatment]. Stud Log Grammar Rhetoric; 2013.35(48):39-48.
17. 17. Uyar A, Bener A, Ciray HN, Bahceci M. [Handling the imbalance problem of IVf implantation prediction]. Int J Comput Sci; 2010.37(2).
18. 18. Cohen G, Hilario M, Sax H, Hugonnet S, Geissbuhler A. [Learning from imbalanced data in surveillance of nosocomial infection]. Artific Intellig Med; 2006.37:7-18.
19. 19. Mazurowski HP, Zuranda MA, Lob J, Baker J, Tourassi G. [Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance]. Neural Networks; 2008.21:427-436.
20. 20. Namee BM, Cunningham P, Byrne S, Corrigan O. [The problem of bias in the data in regression problems in medical decision support]. Artific Intellig Med; 2002.24:51-70.
21. 21. Kozarov G, Stosic L. [Use of semen quality to predict pregnancy in couples undergoing ICSI]. J Women's Health; 2016.1(1):001-004.
22. 22. Van Steirteghem AC, Nagy Z, Joris H, Lio J, Strssen C, Smitz J, et al. [High fertilization and implantation rates after ICSI]. Hum Reprod; 1993.8:1061-66.
23. 23. Friedler S, Raziel A, Strassburger D , Schachter M, Soffer Y, Ron-El R. [Factors influencing the outcome of ICSI I patients with obstructive and non-obstructive azoospermia L: a comparative study]. Hum Reprod; 2002.17:3114-21.
24. 24. Mansour RT, Kamal A, Fahmy I, Tawab N, Serour GI, Aboulghar MA. [Intrcytoplasmic sperm injection in obstructive and non-obstructive azoospermia]. Hum Reprod; 1997.12:1974-79.
25. 25. Gavin HP. [The levenberg-marquardt method for nonlinear least squares curve-fitting problems]. Department of Civil and Environmental Engineering; 2016:1-18.
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