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All Journal IPTEK The Journal for Technology and Science
Saeed Agha Banihashemi
Department of the School of International Relations of Ministry of Foreign Affairs of The Islamic Republic of Iran
Computer Science & IT

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Diagnosis of Heart Disease Using Feature Selection Methods Based On Recurrent Fuzzy Neural Networks Shirin Kordnoori; Hamidreza Mostafaei; Mohsen Rostamy-Malkhalifeh; Mohammadmohsen Ostadrahimi; Saeed Agha Banihashemi
IPTEK The Journal for Technology and Science Vol 32, No 2 (2021)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v32i2.7075

Abstract

The World Health Organization (WHO) estimated one-third of all global deaths reason by cardiovascular diseases. Nowadays, artificial intelligence attracts many considerations in diagnosing heart disease. This study used trained recurrent fuzzy neural networks (RFNN) for diagnosing heart disease. This study also used five kinds of feature selection and extraction models for comparing the action of a model, such as data envelopment analysis (DEA), Linear Discriminative Analysis (LDA), Principle Component Analysis (PCA), Correlation Feature Selection (CFS), and Relief. By using these methods, this paper diagnosed whether the patient has a heart disease problem or not. The results showed that Correlation feature selection has the best operation in feature selection in RFNN by accuracy of 98.4%.
Co-Authors Hamidreza Mostafaei Mohammadmohsen Ostadrahimi Mohsen Rostamy-Malkhalifeh Shirin Kordnoori