A recurrent rough polynomial artificial neural network and its biomedical application to the classification of a cardiac patient

Document Type : Research Paper


Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran


Since the prevalence of cardiovascular disease and consequent mortality has increased, accurate prediction of the disease status of individuals is of great importance. Therefore, models that have the least error and maximum reliability should be used. In recent years, the use of intelligent systems in engineering sciences and mechanics, especially in the prediction of diseases has increased dramatically. The diagnosis of disease by computer systems has become one of the important fields of study for researchers in this field. Diagnosis of heart disease is an attractive and challenging field of research because of the high sensitivity of communities to the death and life of the patient. The use of medical information such as age, gender, blood pressure, blood glucose level, weight, blood cholesterol levels, bio-signal of electrocardiogram, etc. can help physicians in predicting heart disease to prevent the progression of the disease, recurrent heart attacks, and consequently, reduce mortality. This data should be collected in an organized manner and used to integrate the disease prevention and diagnosis system. Evaluating these data and obtaining useful results and patterns in relation to it using data mining techniques and neural networks help to predict the early detection of this disease. This study presents a method of investigating factors influencing heart attacks using the recurrent rough group model of data handling (RRGMDH) neural network. We also compare the results of the proposed method to the results of five model of data handling neural network models, namely long short-term memory (LSTM), gated recurrent unit (GRU), redial basis function (RBF), probabilistic neural network (PNN) and recurrent group model of data handling (GMDH). The results indicate that the proposed method outperforms the five other methods.


[1] M. Abdar, S.R. NiakanKalhori, T. Sutikno, I. Subroto and G. Arji, Comparing performance of data mining algorithms in prediction heart diseases, Int. J. Electr. Comput. Eng. 5 (2015), 1569–1576.
[2] I. Abiodun, A. Jantan, E. Omolara, V. Dada and H. Arshad, State-of-the-art in artificial neural network applications: A survey, Heliyon 4 (2018).
[3] M. Alexandridis and E. Chondrodima, A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing, J. Biomed. Inf. 49 (2014), 61–72.
[4] A.K. Arslan, C. Colak and M.E. Sarihan, Different medical data mining approaches based prediction of ischemic stroke, Comput. Meth. Prog. Biomed. 130 (2016), 87–92.
[5] M. Assaad, R. Bone and H. Cardot, A new boosting algorithm for improved time-series forecasting with recurrent neural networks, Inf. Fusion 354 (2008), 506–526.
[6] P.C. Austin, J.V. Tu, J.E. Ho, D. Levy and D.S. Lee, Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes, J. Clinic. Epidem. 66 (2013), 398–407.
[7] M. Bascil and H. Oztekin, A study on hepatitis disease diagnosis using probabilistic neural network, J. Med. Syst. 36 (2012), no. 3, 1603–1606.
[8] R. Bekhet, Y. Wu, N. Wang, X. Geng and D. Zhi, A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set, J. Biomed. Inf. 84 (2018), 11–16.
[9] H. Benhar and A. Idri, Data preprocessing for heart disease classification: A systematic literature review, Comput. Meth. Prog. Biomed.  195 (2020), 197–210.
[10] F. Beritelli, G. Capizzi, G. Lo Sciuto, C. Napoli and F. Scaglione, Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks, Biomed. Eng. Lett. 8 (2018), no. 1, 77–85.
[11] C. Beulah and S. Carolin, Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques, Inf. Med. Unlocked 16 (2019).
[12] M. Bramer, Principles of data mining, Undergraduate Topics in Computer Science, 2007.
[13] D.S. Bromhead and D. Lowe, Multivariate functional interpolation and adaptive networks, Complex Syst. 2 (1988), 321–355.
[14] S. Chliebs and N. Kasabov, Evolving spiking neural network–A survey, Evolv. Syst. 4 (2013), no. 2, 87–98.
[15] A. Choudhary, J. Harding and M. Tiwari, Data mining in manufacturing: A review based on the kind of knowledge, J. Intell. Manufact. 20 (2009), no. 5, 501–521.
[16] S. Ding and J. Chen, Rough neural networks: A review, J. Comput. Info. Syst. 7 (2011), 2338–2346.
[17] S. Ferrari and F. Stengel, Smooth function approximation using neural networks, IEEE Trans. Neural Networks 16 (2005), 24–38.
[18] R. Fu, Z. Zhang and L. Li, Using LSTM and GRU neural network methods for traffic flow prediction, Youth Acad. Ann. Conf. Chinese Assoc. Automation, 2017, pp. 324–328.
[19] M. Gudadhe, K. Wankhade and S. Dongre, Decision support system for heart disease based on support vector machine and artificial neural network, Int. Conf. Comput. Commun. Tech. IEEE, 2010, pp. 741–745.
[20] J. Han, M. Kamber and J. Pei, Data mining concepts and techniques, Morgan Kaufmann, 2011.
[21] S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9 (1997), 1735–1780.
[22] M.T. Islam, S.R. Rafa and M.G. Kibria, Early prediction of heart disease using PCA and hybrid genetic algorithm with K-means, Int. Conf. Comput. Inf. Tech. 2016, pp. 1–6.
[23] M. Korurek and B. Dogan, ECG beat classification using particle swarm optimization and radial basis function neural network, Expert Syst. Appl. 37 (2010), 7563–7569.
[24] B. Liu, C. Fu, A. Bielefield and Q. Liu, Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network, Energies 10 (2017), no. 10,   1453.
[25] H.M. Lynn, S.B. Pan and P. Kim, A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks, Nat. Res. Found. 7 (2019), 145395–145405.
[26] I. Mahmoudi, R. moghadam, R. Moazzam and S. Saeghian, Identify models predict coronary artery disease using Neural networks and variable selection based on classification and regression tree, J. Shahrekord Univ. Med. Sci. 15 (2014), 47–56.
[27] J. Mair, Puschendorf, J. Smidt, P. Lechleitner and F. Dienst, A decision tree for the early diagnosis of acute myocardial infarction in nontraumatic chest pain patients at hospital admission, CHEST 108 (1995), 1502–1509.
[28] K. Mathan, P. Malarvizh, G. Manogaran and R. Varadharajan, A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease, Design Autom. Embed. Syst. 22 (2018), 225–242.
[29] S. Mehrabi, M. Maghsoudloo, H. Arabalibeik, R. Noormand and Y. Nozari, Application of multilayer perceptron and radial basis function neural networks in differentiating between chronic obstructive pulmonary and congestive heart failure diseases, Expert Syst. Appl. 36 (2009), 6956–6959.
[30] A. Mehrankia, M. Mollakhalili and K. Mirzaie, Prediction of heart attacks using biological signals based on recurrent rough GMDH neural network, J. Neural Process. Lett. 49 (2021), no. 2, 987–1008.
[31] F. Modaresi and S. Araghinejad, A comparative assessment of support vector machines, probabilistic neural networks, and K-nearest neighbor algorithms for water quality classification, Water Resources Manag. 28 (2014), 4095–4111.
[32] J. Moody and C.J. Darken, Fast learning in networks of locally-tuned processing units, Neural Comput. 1 (1989), 281–294.
[33] H. Naderpour, D. Rezazadeh, P. Fakharian, A.H. Rafiean and M. Kalantari, A new proposed approach for moment capacity estimation of ferrocement members using group method of data handling, Eng. Sci. Tech. Int. J. 23 (2020), 382–391.
[34] N. Narimanzadeh, A. Darvizeh, M. Darvizeh and H. Gharababaei, Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition, Mater. Process. Technol. 128 (2002), 80–87.
[35] T.N. Nguyen, S. Lee, H. Nguyen-Xuan and J. Lee, A novel analysis-prediction approach for geometrically nonlinearproblems using group method of data handling, Comput. Meth. Appl. Mech. Engin. 354 (2019), 506–526.
[36] F. Nordin and F. Nagi, Layer-recurrent network in identifying a nonlinear system, Int. Conf. Control Autom. Syst., 2008, pp. 387–391.
[37] L. Oh, K. Ng, S. Tan and R. Acharya, Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats, Comput. Bio. Med. 102 (2018), 278–287.
[38] M.J.D. Powell, Radial basis functions for multi-variable interpolation: A review, Algorithms Approximation, Clarendon Press, 1987.
[39] A. Rady and S. Anwar, Prediction of kidney disease stages using data mining algorithms, Inf. Med. Unlocked 15 (2019).
[40] G.T. Reddy, M.P.K. Reddy, K. Lakshmanna, D.S. Rajput, R. Kaluri and G. Srivastava, Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis, Evolut. Intell. 13 (2020), 185–196.
[41] C. Romero and S. Ventura, Educational data mining: A survey from 1995 to 2005, Expert Syst. Appl. 33 (2007), 135–146.
[42] J.A. Sanz, M. Galar, A. Jurio, A. Brugos, M. Pagola and H. Bustince, Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system, Appl. Soft Comput. 20 (2014), 103–111.
[43] S. Shaghaghi, H. Bonakdari, A. Gholami, I. Ebtehaj and M. Zeinolabedini, Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design, Appl. Math. Comput. 313 (2017), 271–286.
[44] S. Shanmuganathan, Artificial neural network modelling: An introduction, Artific. Neural Network Model. 628 (2016), 1–14.
[45] A. Sharma and P. Mansotra, Emerging applications of data mining for healthcare management–A critical review, Int. Conf. Comput. Sustain. Glob. Dev., 2014, pp. 1–6.
[46] E. Shao, D. Hou and C. Chiu, Hybrid intelligent modeling schemes for heart disease classification, Appl. Soft Comput. 14 (2014), 47–52.
[47] N. Shree and T. Kumar, Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network, Brain Inf. 5 (2018), no. 1, 23–30.
[48] S. Singh, S.K. Pandey, U. Pawar and R. Janghel, Classification of ECG arrhythmia using recurrent neural networks, Proc. Comput. Sci. 132 (2018), 1290–1297.
[49] D. Specht, Probabilistic neural networks, Neural Networks, 3 (1990), 109–118.
[50] P. Strumi l lo and W. Kami´nski, Radial basis function neural networks: Theory and applications, Neural Networks Soft Comput. Physica, Heidelberg, 2003, pp. 107–119.
[51] M. Tayefi, M. Tajfard, S. Saffar, P. Hanachi and M. Moohebati, HS-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm, Comput. Meth. Prog. Biomed. 141 (2017), 105–109.
[52] M. Teshnelab and P. Jafari, Neural networks and neural controllers, Khajeh Nasir al-Din Tusi University of Technology, 2018.
[53] E. Williams, H. Mosley, J. Kop, J. Couper, L. Welch and D. Rosamond, Vital exhaustion as a risk factor for adverse cardiac events (from the atherosclerosis risk in communities [ARIC] study), Amer. J. Cardiol. 105 (2010), 1661–1665.
[54] Z. Wu and S. King, Investigating gated recurrent networks for speech synthesis, Int. Conf. Acoust. Speech Signal Process., 2016, pp. 5140–5144.
[55] D. Wu, K. Warwick, M.N. Gasson and J.G. Burgess, S. Pan and T.Z. Aziz, Prediition of Parkinson’s disease tremor onset using a radial basis function neural network based on particle swarm optimization, Int. J. Neural Syst. 20 (2010), no. 2.
[56] H. Wu, S. Yang, Z. Huang, J. He and X. Wang, Type 2 diabetes mellitus prediction model based on data mining, Inf. Med. Unlocked 10 (2018), 100–107.
[57] J. Xie, B. Chen, X. Gu, F. Liang and X. Xu, Self-attention-based BiLSTM model for short text fine-grained segment classification, Comput. Inf. Process. Comput. Artif. Intel. IEEE 7 (2019), 180558–180570.
[58] H. Yang and Y. PhoebeChen, Data mining in lung cancer pathologic staging diagnosis: Correlation between clinical and pathology information, Expert Syst. Appl. 42 (2015), 6168–6176.
[59] O. Yildirim, U.B. Baloglu, S. Tan, E.J. Ciaccio and U.R. Acharya, A new approach for arrhythmia classification using deep coded features and LSTM networks, Comput. Meth. Prog. Biomed. 176 (2019), 121–133.
Volume 14, Issue 6
June 2023
Pages 221-248
  • Receive Date: 07 July 2022
  • Revise Date: 12 September 2022
  • Accept Date: 18 September 2022