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All Journal IPTEK The Journal for Technology and Science
Mohammadmohsen Ostadrahimi
Department of Mathematics, Tehran North Branch, Islamic Azad University, Tehran, 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%.
The Efficacy of Choosing Strategy with General Regression Neural Network on Evolutionary Markov Games Shirin Kordnoori; Hamidreza Mostafaei; Mohammadmohsen Ostadrahimi; Saeed Agha Banihashemi
IPTEK The Journal for Technology and Science Vol 32, No 1 (2021)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v32i1.7074

Abstract

Nowadays, Evolutionary Game Theory which studies the learning model of players,has attracted more attention than before. These Games can simulate the real situationand dynamic during processing time. This paper creates the Evolutionary MarkovGames, which maps players’ strategy-choosing to a Markov Decision Processes(MDPs) with payoffs. Boltzmann distribution is used for transition probability andthe General Regression Neural Network (GRNN) simulating the strategy-choosing inEvolutionary Markov Games. Prisoner’s dilemma is a problem that uses the methodand output results showing the overlapping the human strategy-choosing line andGRNN strategy-choosing line after 48 iterations, and they choose the same strate-gies. Also, the error rate of the GRNN training by Tit for Tat (TFT) strategy is lowerthan similar work and shows a better res
Co-Authors Hamidreza Mostafaei Hamidreza Mostafaei Mohsen Rostamy-Malkhalifeh Saeed Agha Banihashemi Saeed Agha Banihashemi Shirin Kordnoori