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


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%.


Artificial Intelligent; Feature Selection Model; Heart Disease; Recurrent Fuzzy Neural Networks

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Rakhshan SA. Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method. Journal of the Operational Research Society 2017;68(8):906–918.

Barat M, Tohidi G, Sanei M, Razavyan S. Data envelopment analysis for decision making unit with nonhomogeneous internal structures: An application to the banking industry. Journal of the Operational Research Society 2019;70(5):760–769.

Samuel OW, Asogbon GM, Sangaiah AK, Fang P, Li G. An integrated decision support system based on ANN and FuzzyAHP for heart failure risk prediction. Expert Systems with Applications 2017;68:163–172.

Uyar K, Ilhan A. Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. In: Procedia Computer Science, vol. 120; 2017. p. 588–593.

Louridi N, Amar M, Ouahidi BE. Identification of Cardiovascular Diseases Using Machine Learning. In: 7th Mediterranean Congress of Telecommunications 2019, CMT 2019; 2019. p. 1–6.

Yadav SS, Jadhav SM, Nagrale S, Patil N. Application of Machine Learning for the Detection of Heart Disease. In: 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 - Conference Proceedings; 2020. p. 165–172.

Reddy PK, Reddy SK, Balakrishnan S, Basha SM, Poluru RK. Heart disease prediction using machine learning algorithm. International Journal of Innovative Technology and Exploring Engineering 2019;8(10):2603–2606.

Lin CH, Yang PK, Lin YC, Fu PK. On Machine Learning Models for Heart Disease Diagnosis. In: 2nd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2020, ECBIOS 2020; 2020. p. 158–161.

Sharma V, Rasool A, Hajela G. Prediction of Heart disease using DNN. In: Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020; 2020. p. 554–562.

Li JP, Haq AU, Din SU, Khan J, Khan A, Saboor A. Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare. IEEE Access 2020;8:107562–107582.

Magesh G, Swarnalatha P. Optimal feature selection through a cluster-based DT learning (CDTL) in heart disease prediction. Evolutionary Intelligence 2021;14(2):583–593.

Revathi KR, Kavitha KK. Comparison of classification techniques on heart disease data set. International Journal of Advanced Research in Computer Science 2017 dec;8(9):276–280. http://ijarcs.info/index.php/Ijarcs/article/view/4870.

Buettner R, Schunter M. Efficient machine learning based detection of heart disease. In: 2019 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2019; 2019. p. 1–6.

DOI: http://dx.doi.org/10.12962/j20882033.v32i2.7075


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