نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناس ارشد، مهندسی کامپیوتر، گروه نرم‌افزار، دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران
2 کارشناس، مهندسی کامپیوتر، گروه هوش مصنوعی و رباتیک، دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران
3 استادیار، مهندسی کامپیوتر، گروه نرم‌افزار، دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران
چکیده
مقدمه: با توجه به آمار، امروزه شیوع ناباروری در ایران رو به افزایش است. از طرف دیگر، داده‌کاوی توانسته است با استفاده از داده‌های پزشکی، الگوهای مؤثری را استخراج نماید. پژوهش حاضر با هدف استفاده از داده‌کاوی جهت طراحی سیستمی به منظور پیشنهاد روش درمان ناباروری انجام شد.روش بررسی: این مطالعه از نوع توصیفی- همبستگی بود که روی اطلاعات ثبت شده از 527 زوج نابارور مرکز درمان ناباروری ابن‌سینا تهران انجام گرفت. با بررسی اطلاعات این افراد توسط الگوریتم‌های داده‌کاوی و نرم‌افزار Weka، سیستم PIODEM (Prediction of the best Infertility treatment using Outlier Detection and Ensemble Methods) ارایه شد که شامل سه مرحله بود. ابتدا عوامل مؤثر در انتخاب روش درمان ناباروری با استفاده از تحلیل افتراقی استخراج شد. در مرحله بعد، نمونه‌ها با مقادیر پرت مشخص و ارتباطی بین آن‌ها و انتخاب روش درمان کشف گردید. در نهایت، از رده‌بندهای ترکیبی برای افزایش صحت استفاده شد.یافته‌ها: سیستم پیشنهادی جهت پیش‌بینی روش درمان، موفق به کشف عوامل مؤثری همچون سن مرد، مدت زمان ناباروری، میزان اسپرم‌های بدون حرکت، کاهش غلظت اسپرم، تعداد کل اسپرم، مورفولوژی، مورفولوژی قسمت میانی اسپرم، اسپرم با حرکت سریع و اسپرم با حرکت کند نوع دوم شد. این سیستم مشخص نمود که پرت بودن مقادیر غلظت اسپرم، توکسوپلاسما IgM (Immunoglobulin M)، هورمون 3T (Triiodothyronine) و هورمون TPO (Thyroid Peroxidase) در انتخاب روش درمان تأثیرگذار بود. علاوه بر این، استفاده از الگوریتم‌های ترکیبی، معیار F-measure را تا 76 درصد افزایش داد.نتیجه‌گیری: سیستم PIODEM با استفاده از تحلیل افتراقی و تحلیل داده‌های پرت، قادر به کشف عوامل مؤثر در انتخاب روش درمان می‌باشد. این سیستم با دریافت اطلاعات بیماران به عنوان ورودی، روش درمان را پیشنهاد می‌دهد.
کلیدواژه‌ها
عنوان مقاله [English]
Suggesting the Infertility Treatment Method Using Ensemble Methods and Outlier Analysisthe Infertility Treatment Method Using Ensemble Methods and Outlier Analysis
نویسندگان [English]
- Raana Mahdavi 1
- Samin Fatehi-Raviz 2
- Hossein Rahmani 3
1 MSc, Computer Engineering, Department of Software, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
2 BSc, Computer Engineering, Department of Artificial Intelligence and Robotics, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
3 Assistant Professor, Computer Engineering, Department of Software, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده [English]
Introduction: In recent years, the infertility ratio in young couples has been increased a lot in Iran. From the other side, it has been shown that data mining techniques are capable of extracting novel patterns from medical data. In this study, we proposed a comprehensive system called Prediction of the best Infertility treatment using Outlier Detection and Ensemble Methods (PIODEM) for predicting of the best infertility treatment method for infertile couples.Methods: This descriptive-correlation study used the information of 527 infertile couples, which collected from Avicenna specialized infertility center, Tehran, Iran. PIODEM consists of three steps: First, PIODEM uses the discriminant analysis to find effective factors for choosing the best infertility treatment. Second, PIODEM detects the outlier samples, and applies a correlation between these samples and the choice of treatment method. Third, it uses ensemble methods to increase the precision of classifiers.Results: The PIODEM system succeeded in discovering affective factors such as male-partner’s age, infertility duration, immotile sperm, decreasing of sperm concentration decrease, total sperm count, morphology, sperm motility, sperm with rapid progressive-a motility, and sperm with slow progressive-b motility. Additionally, PIODEM indicates that if one of four features of sperm concentration, toxoplasma immunoglobulin M (IgM), triiodothyronine (T3) hormone, and thyroid peroxidase (TPO) was an outlier, then the prediction of treatment would be more accurate. Finally, using ensemble methods increased the F-measure of PIODEM system by up to 76%.Conclusion: The PIODEM system is able to discover effective factors in the choice of treatment method, using differential analysis and analysis of pert data. This system offers patient information as input for the treatment method.
کلیدواژه‌ها [English]
- Data Mining
- Infertility
- Outlier Analysis
- Ensemble Algorithms
- Turchi P. Prevalence, definition, and classification of infertility. In: Cavallini G, Beretta G, editors. Clinical management of male infertility. Cham, Switzerland: Springer International Publishing; 2015. p. 5-11.
- Gurunath S, Pandian Z, Anderson RA, Bhattacharya S. Defining infertility--a systematic review of prevalence studies. Hum Reprod Update 2011; 17(5): 575-88.
- Kazemijaliseh H, Ramezani TF, Behboudi-Gandevani S, Hosseinpanah F, Khalili D, Azizi F. The prevalence and causes of primary infertility in Iran: A population-based study. Glob J Health Sci 2015; 7(6): 226-32.
- Klitzman R. How much is a child worth? Providers' and patients' views and responses concerning ethical and policy challenges in paying for ART. PLoS One 2017; 12(2): e0171939.
- Bhide A. Fertility treatment: Getting stressed about stress. Acta Obstet Gynecol Scand 2018; 97(3): 233-4.
- Durairaj M, Ramasamy N. Intelligent prediction methods and techniques using disease diagnosis in medical database: A review. International Journal of Control Theory and Applications 2015; 8(5): 2153-60.
- Suzuki T, Perry ACF. Intracytoplasmic sperm injection (ICSI): Applications and insights. In: Palermo GD, Sills ES, editors. Intracytoplasmic sperm injection: Indications, techniques and applications. Cham, Switzerland: Springer International Publishing; 2018. p. 169-81.
- Vaegter KK, Lakic TG, Olovsson M, Berglund L, Brodin T, Holte J. Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers. Fertil Steril 2017; 107(3): 641-8.
- Mckinnon AO, Trounson AO, Silber SJ. Intracytoplasmic sperm injection. In: Samper JC, Pycock JF, Mckinnon AO, editors. Current therapy in equine reproduction. Saint Louis, CA: W.B. Saunders; 2007. p. 296-307.
- Meijerink AM, Cissen M, Mochtar MH, Fleischer K, Thoonen I, de Melker AA, et al. Prediction model for live birth in ICSI using testicular extracted sperm. Hum Reprod 2016; 31(9): 1942-51.
- Ombelet W, Dhont N, Thijssen A, Bosmans E, Kruger T. Semen quality and prediction of IUI success in male subfertility: A systematic review. Reprod Biomed Online 2014; 28(3): 300-9.
- Monraisin O, Chansel-Debordeaux L, Chiron A, Floret S, Cens S, Bourrinet S, et al. Evaluation of intrauterine insemination practices: a 1-year prospective study in seven French assisted reproduction technology centers. Fertil Steril 2016; 105(6): 1589-93.
- Mookim PG, Ellis RP, Kahn-Lang A. Infertility treatment, ART and IUI procedures and delivery. Outcomes: How important is selection? Boston MA: Boston University; 2010. [Unpulished].
- Antoniassi MP, Intasqui P, Camargo M, Zylbersztejn DS, Carvalho VM, Cardozo KH, et al. Analysis of the functional aspects and seminal plasma proteomic profile of sperm from smokers. BJU Int 2016; 118(5): 814-22.
- Milewska AJ, Jankowska D, Cwalina U, Citko D, Wiesak T, Acacio B, et al. Significance of discriminant analysis in prediction of pregnancy in IVF treatment. Studies in Logic, Grammar and Rhetoric 2015; 43(1): 7-20.
- De Giorgi A, Volpi R, Tiseo R, Pala M, Manfredini R, Fabbian F. Seasonal variation of human semen parameters: A retrospective study in Italy. Chronobiol Int 2015; 32(5): 711-6.
- Hinton PR. Statistics explained. London, UK: Routledge; 2014. p. 125-31.
- Mazaheri M, Mohsenian R. Comparison of mental health ratings of fertile and infertile couples. Zahedan J Res Med Sci 2012; 14(1): 72-5. [In Persian].
- Nilforooshan P, Ahmadi SA, Abedi MR, Ahmadi SM. Attitude towards infertility and its relation to depression and anxiety in infertile couples. J Reprod Fertil 2006; 6(5): 546-53.
- Abbasihormozi S, Kouhkan A, Alizadeh AR, Shahverdi AH, Nasr-Esfahani MH, Sadighi Gilani MA, et al. Association of vitamin D status with semen quality and reproductive hormones in Iranian subfertile men. Andrology 2017; 5(1): 113-8.
- Chuang CC, Chen CD, Chao KH, Chen SU, Ho HN, Yang YS. Age is a better predictor of pregnancy potential than basal follicle-stimulating hormone levels in women undergoing in vitro fertilization. Fertil Steril 2003; 79(1): 63-8.
- Gil D, Girela JL, De Juan J, Gomez-Torres MJ, Johnsson M. Predicting seminal quality with artificial intelligence methods. Expert Syst Appl 2012; 39(16): 12564-73.
- Ameri H, Alizadeh S, Hadizadeh M. Assessing the effects of infertility treatment drugs using clustering algorithms and data mining techniques. J Mazandaran Univ Med Sci 2014; 24(114): 26-35. [In Persian].
- Dormohammadi S, Alizadeh S, Asghari M, Shami M. Proposing a prediction model for diagnosing causes of infertility by data mining algorithms. J Health Adm 2014; 17(57): 46-57. [In Persian].
- Hafiz P, Nematollahi M, Boostani R, Namavar JB. Predicting implantation outcome of in vitro fertilization and intracytoplasmic sperm injection using data mining techniques. Int J Fertil Steril 2017; 11(3): 184-90.
- Han J, Pei J, Kamber M. Data mining: Concepts and techniques. Burlington, MA: Morgan Kaufmann; 2011.
- Frank E, Hall MA, Witten IH. The WEKA Workbench. Online Appendix for“Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, Fourth Edition, 2016 [Online]. [cited 2016]; Available from: URL: https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf
- Desai A, Rai S. Analysis of Machine Learning Algorithms using Weka. Proceedings of the International Conference and Workshop on Recent Trends in Technology, (TCET) 2012. International Journal of Computer Applications 2012; 27-32.
)