| Issue |
MATEC Web Conf.
Volume 125, 2017
21st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|
|
|---|---|---|
| Article Number | 02063 | |
| Number of page(s) | 6 | |
| Section | Systems | |
| DOI | https://doi.org/10.1051/matecconf/201712502063 | |
| Published online | 04 October 2017 | |
Comparative Study of Intelligent Systems for Management of GIT Cancers
1 Associate professor at Computer and IS Department, Sadat Academy for Management Sciences.
2 Lecturer at Computer and IS Department, Sadat Academy for Management Sciences.
* Corresponding author: nevmakram@gmail.com
* Corresponding author: edwardwadid@gmail.com
Intelligent Systems contribute in the management of different GIT cancer types. The paper discusses different types of intelligent systems, classified according to the medical task achieved, such as early detection, diagnosis and prognosis. It is found out that these types include rule-based and case-based expert systems, artificial neural networks, genetic algorithms, machine learning, in addition to data mining techniques and statistical methods. The study focuses on comparing between different techniques and tools used. The comparison results in identifying the benefits of using data mining techniques for the diagnosis task, since it is based on huge amounts of data in order to discover new patterns hence new predisposing factors. It also points out the use of expert systems in the prognosis task, since this task is mainly based on the specialist experience that should be transferred to less- experienced medical professionals. Based on the previous results, it is recommended to develop an Intelligent Tutoring System (ITS) that focuses on the early diagnosis of GIT cancers, since managing the disease depends mainly on proper diagnosis, and also to build an expert system that helps transferring GIT cancers management knowledge to medical doctors in different hospitals.
Key words: Intelligent Systems / Data Mining / GIT Cancer / Cancer Management / Expert System / Machine Learning / Artificial Neural Networks / Artificial Intelligence
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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