The study discusses different types of intelligent systems that are being used in the diagnosis, treatment, and prognosis of various GIT cancer types. These intelligent systems include rule-based and case-based expert systems, artificial neural networks, genetic algorithms and machine learning, in addition to data mining techniques and statistical methods. The study aims at identifying different techniques and tools that may be used for each medical task. The results show that data mining techniques were mainly used for the diagnosis task because they rely on huge amounts of data, which may be used to discover new predisposing factor thus improving the diagnosis task. As for expert systems, they may be used in the prognosis task, since they rely on the specialist’s experience. Finally, based on the study results, it is recommended to develop an Intelligent Tutoring System (ITS) that transfers the knowledge of early detection and diagnosis of GIT cancers. As a future work, it is suggested to develop an Expert System (ES) that deals with GIT cancers’ treatment, to be used by medical doctors and specialists in both hospitals and healthcare institutions.
Intelligent Systems, GIT Cancers, Expert Systems, Decision Support Systems, Machine Learning, Artificial Neural Networks, Artificial Intelligence, Genetic Algorithms, Knowledge-based systems, Data Mining
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Cite this paper
Nevine Labib, Edward Morcos. (2017) Intelligent Systems for GIT Cancers Management. International Journal of Biology and Biomedicine, 2, 50-56
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