Hybrid Cascade Forward Neural Network with Elman Neural Network for Disease Prediction

  • Mutasem Sh. AlkhasawnehEmail author
Research Article - Computer Engineering and Computer Science


In this paper, Hybrid Cascade Forward Neural Network with Elman Neural Network (HECFNN) is employed to classify six benchmark medical data sets, viz. Wisconsin Breast Cancer (WBC), Pima Indian Diabetes (PID), Liver Disorder Disease (LDD), Heart Disease (HD), Thyroid Disease (TD) and Cardiotocography (CTG). Three famous performance metrics in medical applications including accuracy, sensitivity and specificity are computed. The results of HECFNN are analyzed and compared with the well-known Elman Neural Network (ENN) and Cascade Forward Neural Network (CFNN). The experimental outcome shows that the HECFNN results outperform those of the CFNN and ENN. Performance outcome for WBC was 97.94% accuracy (ACC), 98.88% specificity (SPE) and 98.84% sensitivity (SEN), while results of PID were 85.10%, 75.61% and 88.39% for ACC, SPE and SEN, respectively, LDD results were ACC 93.80%, SPE 90.09% and SEN 87.50%, HD prediction for ACC, SPE and SEN were 94.01%, 96.71% and 90.40%, respectively, TD obtained 96.10% for ACC, 96.45% for SPE and 96.66% for SEN and CTG achieved 100.00% for SEN, 99.25% for ACC and 98.00% for SPE. In addition, the obtained accuracy of the HECFNN for every benchmark is also compared with those different methods published in the literature review. The results demonstrate that HECFNN produce higher accuracy compared with other well-known methods. In general, the HECFNN experimental results positively demonstrate that the HECFNN is effective and useful in undertaking medical data classification tasks.


Hybrid artificial neural network Elman Neural Network Cascade feed forward neural network Medical decision support Medical data set prediction 


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  1. 1.
    Yan, H.; et al.: A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Syst. Appl. 30(2), 272–281 (2006)Google Scholar
  2. 2.
    Alkhasawneh, M.S.; et al.: Intelligent landslide system based on discriminant analysis and cascade-forward back-propagation network. Arab. J. Sci. Eng. 39(7), 5575–5584 (2014)Google Scholar
  3. 3.
    Suriyal, S.; Druzgalski, C.; Gautam, K.: Mobile assisted diabetic retinopathy detection using deep neural network. In: 2018 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE) (2018)Google Scholar
  4. 4.
    Zhang, X.; et al.: Spatial sequential recurrent neural network for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 99, 1–15 (2018)Google Scholar
  5. 5.
    Flores, G.; Ferreira, V.H.: A rain-streamflow model for prediction of limnimetric behavior of reservoirs using artificial neural networks. In: 2018 Simposio Brasileiro de Sistemas Eletricos (SBSE) (2018)Google Scholar
  6. 6.
    Liu, C.; et al.: A memristor-based neuromorphic engine with a current sensing scheme for artificial neural network applications. In: 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC) (2017)Google Scholar
  7. 7.
    Miller, A.S.; Blott, B.H.; hames, T.K.: Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30(5), 449–464 (1992)Google Scholar
  8. 8.
    Li, Q.; et al.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics and Vision (ICARCV) (2014)Google Scholar
  9. 9.
    Luukka, P.: Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst. Appl. 38(4), 4600–4607 (2011)Google Scholar
  10. 10.
    Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)Google Scholar
  11. 11.
    Demuth, H.; Beale, M.H.; Hagan, M.T.: Neural Network Toolbox User’s Guide. The MathWorks Inc, Natrick (2009)Google Scholar
  12. 12.
    Alkhasawneh, M.S.; Tay, L.T.: A hybrid intelligent system integrating the cascade forward neural network with Elman Neural Network. Arab. J. Sci. Eng. 43, 6737–6749 (2017)Google Scholar
  13. 13.
    Street, W.N.: A neural network model for prognostic prediction. In: ICML (1998)Google Scholar
  14. 14.
    Tourassi, G.D.; et al.: A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med. Phys. 28(5), 804–811 (2001)Google Scholar
  15. 15.
    Maglogiannis, I.; Zafiropoulos, E.; Anagnostopoulos, I.: An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl. Intell. 30(1), 24–36 (2009)Google Scholar
  16. 16.
    Bashir, S.; et al.: HMV: a medical decision support framework using multi-layer classifiers for disease prediction. J. Comput. Sci. 13, 10–25 (2016)Google Scholar
  17. 17.
    Aličković, E.; Subasi, A.: Breast cancer diagnosis using GA feature selection and rotation forest. Neural Comput. Appl. 28(4), 753–763 (2017)Google Scholar
  18. 18.
    Delen, D.; Walker, G.; Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113–127 (2005)Google Scholar
  19. 19.
    Seera, M.; Lim, C.P.: A hybrid intelligent system for medical data classification. Expert Syst. Appl. 41(5), 2239–2249 (2014)Google Scholar
  20. 20.
    Stoean, R.; Stoean, C.: Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Syst. Appl. 40(7), 2677–2686 (2013)MathSciNetGoogle Scholar
  21. 21.
    Kayaer, K.; Yıldırım, T.: Medical diagnosis on Pima Indian diabetes using general regression neural networks. In: Proceedings of the International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP) (2003)Google Scholar
  22. 22.
    Karegowda, A.G.; Manjunath, A.; Jayaram, M.: Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes. Int. J. Soft Comput. 2(2), 15–23 (2011)Google Scholar
  23. 23.
    Kandhasamy, J.P.; Balamurali, S.: Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput. Sci. 47, 45–51 (2015)Google Scholar
  24. 24.
    Erkaymaz, O.; Ozer, M.; Perc, M.: Performance of small-world feedforward neural networks for the diagnosis of diabetes. Appl. Math. Comput. 311, 22–28 (2017)MathSciNetGoogle Scholar
  25. 25.
    Christina, S.S.; Santiago, N.: Decision support system for a chronic disease-diabetes. Int. J. Comput. Math. Sci. 7(3), 126–131Google Scholar
  26. 26.
    Jahangir, M.; et al.: An expert system for diabetes prediction using auto tuned multi-layer perceptron. In: 2017 Intelligent Systems Conference (IntelliSys) (2017)Google Scholar
  27. 27.
    Durairaj, M.; Kalaiselvi, G.: Prediction of diabetes using soft computing techniques-A survey. Int. J. Sci. Technol. Res. 4(3), 190–192 (2015)Google Scholar
  28. 28.
    Liang, C.; Peng, L.: An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J. Med. Syst. 37(2), 9932 (2013)MathSciNetGoogle Scholar
  29. 29.
    Özşen, S.; Güneş, S.: Attribute weighting via genetic algorithms for attribute weighted artificial immune system (AWAIS) and its application to heart disease and liver disorders problems. Expert Syst. Appl. 36(1), 386–392 (2009)Google Scholar
  30. 30.
    Vaidya, M.H.; Chaudhari, M.S.; Ingale, M.H.: Literature review on liver disease classification. Int. J. Adv. Res. Innovative Ideas Educ. 3(3), 4095–4100Google Scholar
  31. 31.
    Wadhonkar, M.B.; Tijare, P.; Sawalkar, S.: Artificial neural network approach for classification of heart disease dataset. Integration 3(4), 388–392 (2014)Google Scholar
  32. 32.
    jabbar, M.A.; Deekshatulu, B.L.; Chandra, P.: Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technol. 10, 85–94 (2013)Google Scholar
  33. 33.
    Dbritto, R.; Srinivasaraghavan, A.; Joseph, V.: Comparative analysis of accuracy on heart disease prediction using classification methods. Int. J. Appl. Info. Syst. 2249–0868 (2016)Google Scholar
  34. 34.
    Malav, A.; Kadam, K.; Kamat, P.: Prediction Of heart disease using k-means and artificial neural network as hybrid approach to improve accuracy. Int. J. Eng. Technol. 9(4), 3081–3085 (2017)Google Scholar
  35. 35.
    Soni, J.; et al.: Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 43–48 (2011)Google Scholar
  36. 36.
    Shouman, M.; Turner, T.; Stocker, R.: Using data mining techniques in heart disease diagnosis and treatment. In: 2012 Japan-Egypt Conference on Electronics, Communications and Computers (2012)Google Scholar
  37. 37.
    Azar, A.T.; Hassanien, A.E.; Kim, T.H.: Expert system based on neural-fuzzy rules for thyroid diseases diagnosis. In: Kim, T.H., Kang, J.J., Grosky, W.I., Arslan, T., Pissinou, N. (eds.) Computer Applications for Bio-technology, Multimedia, and Ubiquitous City, pp. 94–105. Springer, Berlin (2012)Google Scholar
  38. 38.
    Prerana, P.S.; Taneja, Khushboo: Predictive data mining for diagnosis of thyroid disease using neural network. Int. J. Res. Manag. Sci. Technol. 3(2), 75–80 (2015)Google Scholar
  39. 39.
    Margret, J.J.; Lakshmipathi, B.; Kumar, S.A.: Diagnosis of thyroid disorders using decision tree splitting rules. Mol. Biol. 3, 4 (2012)Google Scholar
  40. 40.
    Gharehchopogh, F.S.; Molany, M.; Mokri, F.D.: Using artificial neural network in diagnosis of thyroid disease: a case study. Int. J. Comput. Sci. Appl. (IJCSA) 3, 49–61 (2013)Google Scholar
  41. 41.
    Temurtas, F.: A comparative study on thyroid disease diagnosis using neural networks. Expert Syst. Appl. 36(1), 944–949 (2009)Google Scholar
  42. 42.
    Azar, A.T.; Hassanien, A.E.; Kim, T.-H.: Expert System Based on Neural-Fuzzy Rules for Thyroid Diseases Diagnosis. Springer, Berlin (2012)Google Scholar
  43. 43.
    Pan, Q.; et al.: Improved ensemble classification method of thyroid disease based on random forest. In: 2016 8th International Conference on Information Technology in Medicine and Education (ITME) (2016)Google Scholar
  44. 44.
    Pan, Q.; et al.: Improved ensemble classification method of thyroid disease based on random forest. In: Information Technology in Medicine and Education (ITME), 2016 8th International Conference on IEEE (2016)Google Scholar
  45. 45.
    Prasad, V.; Rao, T.S.; Babu, M.S.P.: Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms. Soft Comput. 20(3), 1179–1189 (2016)Google Scholar
  46. 46.
    Razia, S.; Narasingarao, M.R.; Sridhar, G.R.: A decision support system for prediction of thyroid disease—A comparison of multilayer perceptron neural network and radial basis function neural network. J. Theor. Appl. Inf. Technol. 80, 544–551 (2015)Google Scholar
  47. 47.
    Sahin, H.; Subasi, A.: Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Appl. Soft Comput. 33, 231–238 (2015)Google Scholar
  48. 48.
    Ocak, H.; Ertunc, H.M.: Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems. Neural Comput. Appl. 23(6), 1583–1589 (2013)Google Scholar
  49. 49.
    Georgoulas, G.G.; et al.: Classification of fetal heart rate during labour using hidden Markov models. In: Neural Networks, 2004. Proceedings of 2004 IEEE International Joint Conference on IEEE (2004)Google Scholar
  50. 50.
    Georgoulas, G.; Stylios, C.D.; Groumpos, P.P.: Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines. IEEE Trans. Biomed. Eng. BME 53(5), 875 (2006)Google Scholar
  51. 51.
    Spilka, J.; et al.: Assessment of non-linear features for intrapartal fetal heart rate classification. In: Information Technology and Applications in Biomedicine, 2009. ITAB 2009 9th International Conference on IEEE (2009)Google Scholar
  52. 52.
    Georgoulas, G.; Stylios, C.; Groumpos, P.: Feature extraction and classification of fetal heart rate using wavelet analysis and support vector machines. Int. J. Artif. Intell. Tools 15(03), 411–432 (2006)Google Scholar
  53. 53.
    Magenes, G.; Signorini, M.G.; Arduini, D.: Classification of cardiotocographic records by neural networks. In: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on IEEE (2000)Google Scholar
  54. 54.
    Jang, J.-S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)Google Scholar
  55. 55.
    Lim, C.P.; Goh, W.Y.: The application of an ensemble of boosted Elman networks to time series prediction: a benchmark study. J. Comput. Intell. 3(2), 119–126 (2005)Google Scholar
  56. 56.
    Eddy, P.; Hu, Y.M.; Hung, M.S.: Two-group classification using neural networks. Decis. Sci. 24(4), 825–845 (1993)Google Scholar
  57. 57.
    Alkhasawneh, M.S.; et al.: Determination of important topographic factors for landslide mapping analysis using MLP network. Sci. World J. 2013, 12 (2013)Google Scholar
  58. 58.
    Dheeru, D.; Efi, K.T.: UCI machine learning repository. University of California, Irvine (2017)Google Scholar
  59. 59.
    Bache, K.; Lichman, M.: Cardiotocography data set. UCI Machine Learning Repository (2010)Google Scholar
  60. 60.
    Jung, Y.; Hu, J.: A k-fold averaging cross-validation procedure. J. Nonparametric Statistics. 27(2), 167–179 (2015)MathSciNetzbMATHGoogle Scholar
  61. 61.
    Loo, C.K.: Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP. IEEE Trans. Knowl. Data Eng. 17(11), 1589–1593 (2005)Google Scholar
  62. 62.
    Örkcü, H.H.; Bal, H.: Comparing performances of backpropagation and genetic algorithms in the data classification. Expert Syst. Appl. 38(4), 3703–3709 (2011)Google Scholar
  63. 63.
    Kalaiselvi, C.; Nasira, G.M.: A new approach for diagnosis of diabetes and prediction of cancer using ANFIS. In: 2014 World Congress on Computing and Communication Technologies (2014)Google Scholar
  64. 64.
    Abdar, M.; et al.: Performance analysis of classification algorithms on early detection of liver disease. Expert Syst. Appl. 67, 239–251 (2017)Google Scholar
  65. 65.
    Khemphila, A.; Boonjing, V.: Heart disease classification using neural network and feature selection. In: Systems Engineering (ICSEng), 2011 21st International Conference on IEEE (2011)Google Scholar
  66. 66.
    Ebenezer, O.; Oyebade, K.; Khashman, A.: Heart diseases diagnosis using neural network arbitration. Int. J. Intell. Syst. Appl. 7(12), 75–82 (2015)Google Scholar
  67. 67.
    Samuel, O.W.; et al.: An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst. Appl. 68, 163–172 (2017)Google Scholar
  68. 68.
    Cömert, Z.; Kocamaz, A.F.; Güngör, S.: Cardiotocography signals with artificial neural network and extreme learning machine. In: Signal Processing and Communication Application Conference (SIU), 2016 24th IEEE (2016)Google Scholar

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© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Software Engineering Department, Faculty of Information and TechnologyAjloun National UniversityAjlounJordan

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