IJE TRANSACTIONS C: Aspects Vol. 31, No. 12 (December 2018) 1961-1962   

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S. Moameri and N. Samadiani
( Received: June 08, 2018 – Accepted: November 26, 2018 )

Abstract    In this paper, we propose a novel fuzzy expert system for detection of Coronary Artery Disease, using cuckoo search algorithm. This system includes three phases: firstly, at the stage of fuzzy system design, a decision tree is used to extract if-then rules which provide the crisp rules required for Coronary Artery Disease detection. Secondly, the fuzzy system is formed by setting the intervals for fuzzy variables and extracted rules. Finally, Cuckoo Search algorithm is used to optimize fuzzy membership functions. The accuracy of our proposed system is evaluated using Cleveland Cardiac Patient Database. The detection rate is 93.48% employing optimized membership functions. Also, 85.76% accuracy is obtained for predicting the risk of coronary artery disease. The superiority of proposed system is obvious by comparing it to the previously methods; it is more accurate and is also easier to implement.


Keywords    Coronary artery disease,Fuzzy system,Cuckoo search,Heart disease,Decision tree


چکیده    در این مقاله، یک سیستم خبره فازی جدید برای تشخیص بیماری عروق کرونر با استفاده از الگوریتم تکاملی فاخته (CS) پیشنهاد شده است. این سیستم در سه مرحله این بیماری را تشخیص می‌دهد: ابتدا در مرحله‌ی طراحی سیستم فازی، یک درخت تصمیم برای استخراج قوانین اگر-آنگاه موردنیاز برای پیش بینی بیماری عروق کرونر، استفاده می‌شود. سپس در مرحله‌ی دوم، سیستم فازی با مقداردهی اولیه‌ی بازه‌های مقادیر فازی و قوانین مستخرج از مرحله‌ی قبل، ساخته می‌شود. در پایان، الگوریتم فاخته برای بهینه کردن توابع عضویت فازی مورد استفاده قرار می‌گیرد. دقت سیستم پیشنهادی با رکوردهای پایگاه داده کلیولند ارزیابی می‌شود. سیستم پیشنهادی قادر است با دقت 93.48%، بیماری عروق کرونر را تشخیص دهد. هم چنین، دقت 85.76% برای پیش بینی میزان ریسک این بیماری به دست آمد. برتری سیستم پیشنهادی با پیاده سازی ساده تر و دقت بیشتر در مقایسه با سایر پژوهش‌های موجود در این حوزه، کاملاً آشکار است

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