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العنوان
Using Data Mining Techniques to Improve the
Performance of Decision Support Systems /
المؤلف
Eliwa, Entesar Hamed Ebrahim.
هيئة الاعداد
باحث / انتصار حامد إبراهيم عليوة
مشرف / محب رمزي جرجس
مشرف / طارق مصطفى محمود
مشرف / أحمد سويلم أحمد
الموضوع
Computer science. Computer science - Study and teaching.
تاريخ النشر
2014.
عدد الصفحات
163 P. :
اللغة
الإنجليزية
الدرجة
الدكتوراه
التخصص
علوم الحاسب الآلي
تاريخ الإجازة
1/1/2014
مكان الإجازة
جامعة المنيا - كلية العلوم - علوم الحاسب
الفهرس
Only 14 pages are availabe for public view

from 125

from 125

Abstract

Today diagnosing patients correctly and administering effective
treatments have become quite a challenge. Poor clinical decisions may end to
patient death and which cannot be afforded by the hospital as it loses its
reputation. The cost to treat a patient with a heart problem is quite high and
not affordable by every patient. To achieve a correct and cost effective
treatment computer-based information and/or decision support systems can
be developed to do the task.
A Medical Decision Support System (MDSS) is a computer-based
information system that supports medical organizational decision-making
activities. MDSSs serve the management, operations, physicians, and
planning levels of any medical organization (usually mid and higher
management) and help to make decisions.
Data Mining is one of the most important areas of research that is
become increasingly common in medical organization. It plays an important
role for discovering new trends in healthcare organization which in turn
helpful for all the parties associated with this field. Data mining can automate
the process of finding relationships and patterns in raw data and the results
can be either utilized in an automated decision support system or assessed by
a human analyst.
The decision support systems employing data mining tools can find new
and unsuspected patterns and relationships in a given set of data; the system
then applies this newly discovered knowledge to a new set of data. This is
most useful when a priori knowledge is limited or nonexistent.