عنوان المقالة:تصنيف بيانات تخطيط القلب على أساس الشبكة العصبية الإستقرابية Classifying Cardiotocography Data based on Rough Neural Network
أ.د. أحمد سلامة | Prof. Dr. Ahmed Salama | 10500
نوع النشر
مقال علمي
المؤلفون بالعربي
أحمد سلامة- بلال أمين- الحناوى- خالد محفوظ-منى أمين
المؤلفون بالإنجليزي
AA Salama, Belal Amin, Khaled Mahfouz, Mona Gamal
الملخص الانجليزي
Cardiotocography is a medical device that monitors fetal heart rate and the uterine contraction during the period of pregnancy. It is used to diagnose and classify a fetus state by doctors who have challenges of uncertainty in data. The Rough Neural Network is one of the most common data mining techniques to classify medical data, as it is a good solution for the uncertainty challenge. This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and consumed time during the classification process. WEKA tool is used to analyse cardiotocography data with different algorithms (neural network, decision table, bagging, the nearest neighbour, decision stump and least square support vector machine algorithm). The comparison shows that the accuracy rates and time consumption of the proposed model are feasible and efficient.
تاريخ النشر
01/12/2019
الناشر
International Journal of Advanced Computer Science and Applications
رقم المجلد
10
رقم العدد
8
رابط الملف
تحميل (141 مرات التحميل)
الكلمات المفتاحية
Accuracy rate; cardiotocography; data mining; rough neural network; WEKA tool
رجوع