Hybrid Gravitational Search and Particle Swarm Based Fuzzy MLP for Medical Data Classification

  • Tirtharaj Dash
  • Sanjib Kumar Nayak
  • H. S. Behera
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


In this work, a hybrid training algorithm for fuzzy MLP, called Fuzzy MLP-GSPSO, has been proposed by combining two meta-heuristics: gravitational search (GS) and particle swarm optimization (PSO). The result model has been applied for classification of medical data. Five medical datasets from UCI machine learning repository are used as benchmark datasets for evaluating the performance of the proposed ‘Fuzzy MLP-GSPSO’ model. The experimental results show that Fuzzy MLP-GSPSO model outperforms Fuzzy MLP-GS and Fuzzy MLP-PSO for all the five datasets in terms of classification accuracy, and therefore can reduce overheads in medical diagnosis.


Fuzzy multilayer perceptron Gravitational search Particle swarm optimization Breast cancer Heart disease Hepatitis Liver disorder Lung cancer Classification Medical data 


  1. 1.
    Fan, C.-Y., Chang, P.-C., Lin, J.-J., Hsieh, J.C.: A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl. Soft Comput. 11, 632–644 (2011)CrossRefGoogle Scholar
  2. 2.
    Bojarczuk, C.-C., Lopes, H.-S., Freitas, A.-A.: Genetic programming for knowledge discovery in chest-pain diagnosis. IEEE Eng. Med. Biol. Mag. 19(4), 38–44 (2000)CrossRefGoogle Scholar
  3. 3.
    Floyd, C.E., Lo, J.Y., Yun, A.J., Sullivan, D.C., Kornguth, P.J.: Prediction of breast cancer malignancy using an artificial neural network. Cancer 74, 2944–2998 (1994)CrossRefGoogle Scholar
  4. 4.
    Wu, Y.-Z., Giger, M.-L., Doi, K., Vyborny, C.J., Schmidt, R.A., Metz, C.E.: Artificial neural networks in mammography: application and decision making in the diagnosis of breast cancer. Radiology 187, 81–87 (1993)CrossRefGoogle Scholar
  5. 5.
    Setiono, R., Huan, L.: Understanding neural networks via rule extraction. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 480–487, Morgan Kauffman, San Mateo, CA (1995)Google Scholar
  6. 6.
    Setiono, R.: Generating concise and accurate classification rules for breast cancer diagnosis. Artif. Intell. Med. 18, 205–219 (2000)CrossRefGoogle Scholar
  7. 7.
    Fogel, D.B., Wasson, E.C., Boughton, E.M.: Evolving neural networks for detecting breast cancer. Cancer Lett. 96(1), 49–53 (1995)CrossRefGoogle Scholar
  8. 8.
    Pulkkinen, P., Koivisto, H.: Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods. Appl. Soft Comput. 7, 520–533 (2007)CrossRefGoogle Scholar
  9. 9.
    Chang, P.C., Liao, T.W.: Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory. Appl. Soft Comput. 6(2), 198–206 (2006)CrossRefGoogle Scholar
  10. 10.
    Song, X.-N., Zheng, Y.-J., Wud, X.-J., Yang, X.-B., Yang, J.-Y.: A complete fuzzy discriminant analysis approach for face recognition. Appl. Soft Comput. 10, 208–214 (2010)CrossRefGoogle Scholar
  11. 11.
    Gadaras, I., Mikhailov, L.: An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif. Intell. Med. 47(1), 25–41 (2009)CrossRefGoogle Scholar
  12. 12.
    Lee, C.S., Wang, M.H.: Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition. Expert Syst. Appl. 33(3), 606–619 (2007)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Polat, K., Gunes, S., Arslan, A.: A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Expert Syst. Appl. 34(1), 482–487 (2008)CrossRefGoogle Scholar
  14. 14.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)CrossRefzbMATHGoogle Scholar
  15. 15.
    Eberhart, R., Kennedym, J.: A new optimization using particle swarm theory. In: Sixth International Symposium on Micro Machine and Human Science, MHS’95, pp. 39–43, IEEE (1995)Google Scholar
  16. 16.
    Dash, T., Behera, H.S.: Fuzzy MLP approach for non-linear pattern classification. In: International Conference on Communication and Computing (ICC-2014), Bangalore, India. Computer Networks and Security, pp. 314–323, Elsevier Publications (2014)Google Scholar
  17. 17.
    Bache, K., Lichman, M.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA, (2013)

Copyright information

© Springer India 2015

Authors and Affiliations

  • Tirtharaj Dash
    • 1
  • Sanjib Kumar Nayak
    • 2
  • H. S. Behera
    • 2
  1. 1.School of Computer ScienceNational Institute of Science and TechnologyBerhampurIndia
  2. 2.Department of Computer Science Engineering and Information TechnologyVeer Surendra Sai University of TechnologyBurlaIndia

Personalised recommendations