LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,141
تعداد مقالات10,440
تعداد مشاهده مقاله44,739,860
تعداد دریافت فایل اصل مقاله10,540,095
Mostaar, A, Sattari, M R, Hosseini, S, Deevband, M R. (1398). Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method. سامانه مدیریت نشریات علمی, 9(6), 679-386. doi: 10.31661/jbpe.v0i0.1187
A Mostaar; M R Sattari; S Hosseini; M R Deevband. "Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method". سامانه مدیریت نشریات علمی, 9, 6, 1398, 679-386. doi: 10.31661/jbpe.v0i0.1187
Mostaar, A, Sattari, M R, Hosseini, S, Deevband, M R. (1398). 'Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method', سامانه مدیریت نشریات علمی, 9(6), pp. 679-386. doi: 10.31661/jbpe.v0i0.1187
Mostaar, A, Sattari, M R, Hosseini, S, Deevband, M R. Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method. سامانه مدیریت نشریات علمی, 1398; 9(6): 679-386. doi: 10.31661/jbpe.v0i0.1187

Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method

Journal of Biomedical Physics and Engineering
مقاله 12، دوره 9، شماره 6، اسفند 2019، صفحه 679-386    اصل مقاله (889.66 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1187
نویسندگان
A Mostaar* 1، 2؛ M R Sattari3؛ S Hosseini4؛ M R Deevband1
1PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2PhD, Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
3MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4PhD, Preventive Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Intracytoplasmic sperm injection (ICSI) or microinjection is one of the most commonly used assisted reproductive technologies (ART) in the treatment of patients with infertility problems. At each stage of this treatment cycle, many dependent and independent variables may affect the results, according to which, estimating the accuracy of fertility rate for physicians will be difficult.
Objective: This study aims to evaluate the efficiency of artificial neural networks (ANN) and principal component analysis (PCA) to predict results of infertility treatment in the ICSI method.
Material and Methods: In the present research that is an analytical study, multilayer perceptron (MLP) artificial neural networks were designed and evaluated to predict results of infertility treatment using the ICSI method. In addition, the PCA method was used before the process of training the neural network for extracting information from data and improving the efficiency of generated models. The network has 11 to 17 inputs and 2 outputs.
Results: The area under ROC curve (AUC) values were derived from modeling the results of the ICSI technique for the test data and the total data. The AUC for total data vary from 0.7670 to 0.9796 for two neurons, 0.9394 to 0.9990 for three neurons and 0.9540 to 0.9906 for four neurons in hidden layers.
Conclusion: The proposed MLP neural network can model the specialist performance in predicting treatment results with a high degree of accuracy and reliability.
کلیدواژه‌ها
Neural Networks؛ Principal Component Analysis؛ Fertility؛ Intracytoplasmic Sperm Injection
مراجع
  1. Gurunath S, Pandian Z, Anderson RA, Bhattacharya S. Defining infertility--a systematic review of prevalence studies. Hum Reprod Update. 2011;17:575-88. doi: 10.1093/humupd/dmr015. PubMed PMID: 21493634.
  2. Behjati-Ardakani Z, Akhondi MM, Mahmoodzadeh H, Hosseini SH. An Evaluation of the Historical Importance of Fertility and Its Reflection in Ancient Mythology. J Reprod Infertil. 2016;17:2-9. PubMed PMID: 26962477; PubMed Central PMCID: PMCPMC4769851.
  3. Chen CC, Hsu CC, Cheng YC, Li ST, editors. Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization. 22-24 June 2009. Taichung: Ninth IEEE International Conference on Bioinformatics and BioEngineering; 2009
  4. 4. Zegers-Hochschild F, Adamson GD, De Mouzon J, Ishihara O, Mansour R, Nygren K, et al. The International Committee for Monitoring Assisted Reproductive Technology (ICMART) and the World Health Organization (WHO) Revised Glossary on ART Terminology, 2009. Hum Reprod. 2009;24:2683-7. doi: 10.1093/humrep/dep343. PubMed PMID: 19801627.
  5. Guh R-S, Wu T-CJ, Weng S-P. Integrating genetic algorithm and decision tree learning for assistance in predicting in vitro fertilization outcomes. Expert Systems with Applications. 2011;38:4437-49. doi: 10.1016/j.eswa.2010.09.112.
  6. Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. PLoS Med. 2012;9:e1001356. doi: 10.1371/journal.pmed.1001356. PubMed PMID: 23271957; PubMed Central PMCID: PMCPMC3525527.
  7. Thoma ME, McLain AC, Louis JF, King RB, Trumble AC, Sundaram R, et al. Prevalence of infertility in the United States as estimated by the current duration approach and a traditional constructed approach. Fertil Steril. 2013;99:1324-31 e1. doi: 10.1016/j.fertnstert.2012.11.037. PubMed PMID: 23290741; PubMed Central PMCID: PMCPMC3615032.
  8. Akhondi MM, Kamali K, Ranjbar F, Shirzad M, Shafeghati S, Behjati Ardakani Z, et al. Prevalence of Primary Infertility in Iran in 2010. Iran J Public Health. 2013;42:1398-404. PubMed PMID: 26060641; PubMed Central PMCID: PMCPMC4441936.
  9. Women’s NCCf, Health Cs. Fertility: assessment and treatment for people with fertility problems. London: RCOG press; 2004.
  10. Leushuis E, Van Der Steeg JW, Steures P, Bossuyt PM, Eijkemans MJ, Van Der Veen F, et al. Prediction models in reproductive medicine: a critical appraisal. Hum Reprod Update. 2009;15:537-52. doi: 10.1093/humupd/dmp013. PubMed PMID: 19435779.
  11. Nardelli AA, Stafinski T, Motan T, Klein K, Menon D. Assisted reproductive technologies (ARTs): evaluation of evidence to support public policy development. Reprod Health. 2014;11:76. doi: 10.1186/1742-4755-11-76. PubMed PMID: 25376649; PubMed Central PMCID: PMCPMC4233043.
  12. Moolenaar LM, Vijgen SM, Hompes P, Van Der Veen F, Mol BW, Opmeer BC. Economic evaluation studies in reproductive medicine: a systematic review of methodologic quality. Fertil Steril. 2013;99:1689-94. doi: 10.1016/j.fertnstert.2012.12.045. PubMed PMID: 23395364.
  13. Rosenwaks Z, Palermo GD. Intracytoplasmic sperm injection. Surgical and Medical Management of Male Infertility. 2013;237.
  14. Yan H, Jiang Y, Zheng J, Peng C, Li Q. A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications. 2006;30:272-81. doi: 10.1016/j.eswa.2005.07.022.
  15. 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;17:68-81. PubMed PMID: 27141461; PubMed Central PMCID: PMCPMC4842237.
  16. Hunault CC, Eijkemans MJ, Pieters MH, Te Velde ER, Habbema JD, Fauser BC, et al. A prediction model for selecting patients undergoing in vitro fertilization for elective single embryo transfer.
  17. Fertil Steril. 2002;77:725-32. doi: 10.1016/s0015-0282(01)03243-5. PubMed PMID: 11937124.
  18. Kaufmann SJ, Eastaugh JL, Snowden S, Smye SW, Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization. Hum Reprod. 1997;12:1454-7. doi: 10.1093/humrep/12.7.1454. PubMed PMID: 9262277.
  19. Milewski R, Jamiolkowski J, Milewska Anna J, Domitrz J, Szamatowicz J, Wolczynski S. [Prognosis of the IVF ICSI/ET procedure efficiency with the use of artificial neural networks among patients of the Department of Reproduction and Gynecological
  20. Endocrinology]. Ginekol Pol. 2009;80:900-6. PubMed PMID: 20120934.
  21. Wald M, Sparks A, Sandlow J, Van-Voorhis B, Syrop CH, Niederberger CS. Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa. Reprod Biomed Online. 2005;11:325-31. doi: 10.1016/s1472-6483(10)60840-1. PubMed PMID: 16176672.
  22. Hagan M, Demuth H, Beale M, De Jesús O. Neural network design. 2nd edition. Oklahoma: Oklahoma State University. 2014.
  23. Jolliffe I. Principal component analysis. New York: Springer; 2011. p. 1094-6.
  24. Milewski R, Jankowska D, Cwalina U, Milewska AJ, Citko D, Więsak T, et al. 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. 2016;47:33-46. doi: 10.1515/slgr-2016-0045.
  25. Ioele G, De Luca M, Dinc E, Oliverio F, Ragno G. Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations. Chem Pharm Bull (Tokyo). 2011;59:35-40. doi: 10.1248/cpb.59.35. PubMed PMID: 21212544.
  26. Chen D-f, Ji Q-c, Zhao L, Zhang H-c. The classification of wine based on pca and ann. Fuzzy Information and Engineering Volume 2: Springer; 2009. p. 647-55. doi: 0.1007/978-3-642-03664-4_71.
  27. Buciński A, Bączek T, Krysiński J, Szoszkiewicz R, Załuski J. Clinical data analysis using artificial neural networks (ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy. Reports of Practical Oncology & Radiotherapy. 2007;12:9-17. doi: 10.1016/s1507-1367(10)60036-3.
  28. Jilani TA, Yasin H, Yasin MM. PCA-ANN for classification of Hepatitis-C patients. International Journal of Computer Applications. 2011;14:1-6.
  29. doi: 10.5120/1899-2530.
آمار
تعداد مشاهده مقاله: 1,284
تعداد دریافت فایل اصل مقاله: 556


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب