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
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