A GA Approach for Tuning Membership Functions of a Fuzzy Expert System for Heart Disease Prognosis Development Risk
Publish place: Journal of Computing and Security، Vol: 4، Issue: 1
Publish Year: 1396
نوع سند: مقاله ژورنالی
زبان: English
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JR_JCSE-4-1_002
تاریخ نمایه سازی: 8 دی 1400
Abstract:
Application of soft computing hybrid models have been concentrated to cope with uncertainty in the medical expert systems, recently. Heart disease is one of the mortal diseases that can be controlled in early stages. In this paper a hybrid Fuzzy-GA model for the Heart Disease Prediction (HDP) problem has been proposed. For this, first a Fuzzy Expert System (FES) using Mamdani model was presented. Then the membership functions parameters of the FES were optimized using the hybrid Fuzzy-Genetic Algorithm (Fuzzy-GA). The reason of selecting fuzzy method was its high potential to address the uncertainty sources in the knowledge of medical experts. Performance of the FES and Fuzzy-GA model were evaluated using a real dataset of ۳۸۰ patients collected from Parsian Hospital in Karaj, Iran. Accuracy of the designed FES before optimization was ۸۵.۵۲%. After optimization using the hybrid Fuzzy-GA, the accuracy of this system was increased to ۹۲.۳۷%. The proposed hybrid model competes with its counterparts in terms of interpretability and accuracy in prognosis process of the heart disease. This model is promising for early diagnosis of the heart disease and saving more people lives.
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Authors
Rana Akhoondi
Department of Computer Engineering, Shahr-e-Qods branch, Islamic Azad University, Tehran, Iran.
Rahil Hosseini
Department of Computer Engineering, Shahr-e-Qods branch, Islamic Azad University, Tehran, Iran.
Mahdi Mazinani
Department of Electronic Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.
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