پیش بینی اهداء خون با استفاده از داده کاوی بر پایه الگوریتم های درخت تصمیم، KNN، SVM و MLP

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد تحقیق در عملیات، دانشگاه خوارزمی، تهران.

2 استادیار گروه مدیریت فناوری اطلاعات، دانشگاه خوارزمی، تهران

چکیده

اهدای خون به دلیل نقش حیاتی و حساسی که در امر حفظ سلامت و بقاء زندگی انسان دارد مورد توجه می‌باشد. در جهان امروز علیرغم تحول عظیم علمی و با وجود پیشرفت‌های بزرگی که در علوم پزشکی رخ داده است، هنوز تامین کافی خون سالم یکی از چالش‌ها و دغدغه‌های مجامع پزشکی جهان است. حفظ و تامین حجم خون مورد نیاز در بانک‌های خون هر مرکز انتقال خون در هر منطقه، گروه‌های متنوع خونی و ارتباطاتی که بین آن‌ها وجود دارد و با فرض اینکه یکسری گروه‌های خونی کمیاب‌تر می‌باشند، پیش بینی و برنامه ریزی اهداء خون را در طول زمان مهمتر و پیچیده‌تر می کند. استفاده از داده کاوی در پایگاه‌های داده بیمارستان‌ها و مراکز انتقال خون به کشف روابط کمک می‌کند تا آن‌ها بتوانند بر مبنای گذشته یک پیش بینی از آینده داشته باشند، و بتوانند به بهترین شکل برای کمک، تشخیص و درمان‌های پزشکی موفق بیماری‌های مختلف را شناسایی کرده و الگوهای جراحات جدید را نشان دهند. در این مقاله سعی شدهاست تا در سطوح تصمیم گیری مربوط به حوزه مذکور، از تکنیک‌های داده کاوی و یادگیری ماشین برای پیش بینی اهداء خون استفاده شود تا با استفاده از این مکانیزم بتوانیم پیش بینی کنیم که در بازه‌های زمانی مختلف، چه میزان خون به بانک‌ها و مراکز انتقال خون اهداء خواهد شد که در این صورت بتوانیم حجم خون مورد نیاز بانک‌های خون مناطق مختلف را تخمین و تامین نمائیم. در همین راستا از چند الگوریتم طبقه بندی در یادگیری با نظارت از جمله الگوریتم‌های درخت تصمیم، KNN، SVM و MLP که یکی از انواع شبکه‌های مصنوعی عصبی (ANN) می باشد، برای پیش بینی استفاده شده و نتایج میزان دقت هر کدام ارائه شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of Blood Donations Using Data Mining Based on the Decision Tree Algorithms KNN, SVM, and MLP

نویسندگان [English]

  • Arash Fahmihassan 1
  • Mohammadreza Moghari 1
  • Omidmahdi Ebadati 2
1 Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, Iran
2 Information Technology Management department ,Kharazmi University, Tehran, Iran
چکیده [English]

Blood donation has an important and critical role to preserve the health and survival of human life. In today's world, despite the enormous scientific advancements and the great developments in medical sciences, adequate supply of healthy blood is one of the challenges and concerns of the medical community in the world. Preserving and supplying the volume of blood required in blood banks of each region, and the diverse blood groups with the connections between them, with assuming that the number of blood groups are rarer; makes the prediction and planning of blood donation more and more complicated and important during the time. The use of data mining in hospitals and blood transfer centers databases helps in the discovery of relations, so that they can have a future prediction based on the past information. Accordingly, they have better diagnosed and successful cure various illnesses and show the patterns of new injuries. In this paper, we try to use data mining and machine learning techniques in decision making levels at mentioned field, to use this mechanism for prediction that how much blood will be donate to blood transfusion centers and blood banks in different period time, to estimate and supply the required blood volume of blood banks in different areas. In this regard, we use several classification algorithms in supervised learning for the prediction, including decision tree algorithms, KNN, SVM and MLP, these algorithms are implemented to predict and results of accuracy are presented.

کلیدواژه‌ها [English]

  • Data Mining
  • Machine Learning
  • Decision Tree Algorithms

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