Univrsity of Tehran Press Industrial Management Journal 3115-7386 9 2 2017 07 23 A Fuzzy Expert System for Diagnosis of Epilepsy Diseases Using the Situational Logic and ACH Modeling in the Creation of Knowledge Base ارائۀ یک سیستم خبرۀ فازی برای تشخیص نوع بیماری صرع با استفاده از منطق وضعیت و مدل ACH در ایجاد پایگاه دانش 353 382 64588 10.22059/imj.2017.234113.1007241 EN Ali Amooji Ph.D in Computer Engineering and Information System, Payame Noor University, Tehran, Iran Abdolhamid Fetanat Department of computer, faculty of electric and computer engineering, Azad Islam University Mahshahr branch, Mahshahr, Iran Journal Article 2017 05 27 <span class="alt-edited">Nowadays</span> expert systems are used as one of the most useful and most practical decision support systems. These systems <span class="alt-edited">are relying on</span> knowledge of experts in certain domain combines valuable experience with speed and accuracy of computer and improve the quality of their judgments. One of the most extensive <span class="alt-edited">applications of these systems is</span> medical <span class="alt-edited">diagnostic fields</span>. Different from what happened in most <span class="alt-edited">prior researches</span> in the development of expert systems, in the present study <span class="alt-edited">using</span> the situational logic in the process of knowledge acquisition and fuzzy inference engine architecture approach is recommended. <span class="alt-edited">Identify the type of</span><span class="shorttext"> epilepsy</span> has always been one of the most challenging deliberations <span class="alt-edited">among the</span> neurologist doctors and strict distinction between the types of the disease, according to the <span class="alt-edited">closely</span> signs <span class="shorttext">Creates </span><span class="alt-edited">conflict</span> among the field's <span class="alt-edited">doctors that the</span> expert system is able to solve this problem with the accuracy of 83 percent. <span class="alt-edited">Forming</span> <span class="alt-edited">a comprehensive</span> knowledge base using <span class="alt-edited">analysis of</span> <span class="alt-edited">competing</span> <span class="alt-edited">hypotheses</span> (ACH) modeling in order to distinguish between 14 types of epilepsy disease is the distinctive features of this study. Research done on <span class="alt-edited">the</span> project can be used to <span class="alt-edited">diagnose</span> other diseases that have similar symptoms closely and to be pragmatic. The proposed system can be used in situations where access to neurologist doctors is <span class="alt-edited">impossible </span>can be very useful. https://imj.ut.ac.ir/article_64588_a7534d69e85734eae22425b3b15fdf64.pdf