Diagnosis of Heart Disease Using Feature Selection Methods Based On Recurrent Fuzzy Neural Networks

Shirin Kordnoori (Department of Computer Engineering-Artificial Intelligence, North Tehran Branch, Islamic Azad University, Tehran, Iran)
Hamidreza Mostafaei (Department of Statistics, Tehran North Branch, Islamic Azad University, Tehran, Iran)
Mohsen Rostamy-Malkhalifeh (Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Mohammadmohsen Ostadrahimi (Department of Mathematics, Tehran North Branch, Islamic Azad University, Tehran, Iran)
Saeed Agha Banihashemi (Department of the School of International Relations of Ministry of Foreign Affairs of The Islamic Republic of Iran)

IPTEK The Journal for Technology and Science
Vol 32, No 2 (2021)

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%.