Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3889–3918 | Cite as

Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis



In this paper, we introduce a novel hybrid method that uses a genetic algorithm (GA) in combination with a swarm optimization algorithm (particle swarm optimization (PSO) or fruit fly optimization algorithm (FOA)) for medical diagnosis. The proposed approaches, called GAPSO-FS and GAFOA-FS, simultaneously employ the genetic algorithm (GA) to choose the optimal feature subset and the swarm optimization algorithms (PSO/FOA) to optimize the SVM parameters. This procedure primarily comprises three synchronized parallel layers, including two optimization layers and an intermediate layer. The intermediate layer is mainly responsible for harmonizing the information from the two optimization layers and then distributing the processed information back to those layers. The major contribution of the proposed approaches is that they fully exploit the advantages of the different algorithms. The genetic algorithm (GA) excels at selecting the optimal feature subset, whereas swarm optimization algorithms (PSO/FOA) are optimal for searching the most appropriate continuous variables, including the penalty parameter C and the hyperplane parameter. We performed several groups of experiments on real-world medical cases from the UCI machine learning data repository to compare our hybrid approaches with well-known optimization techniques. The empirical results demonstrated that the proposed GAPSO-FS and GAFOA-FS can select the best SVM model parameters and a more highly relevant feature subset for the SVM classifier than a single algorithm can, thus improving the classification performance when solving a medical diagnosis problem. Therefore, the proposed approach has potential as a useful tool in medical diagnosis.


feature selection medical diagnosis fruit fly optimization particle swarm optimization SVM optimization 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal studies

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Achayuthakan C, Ongsakul W (2009) TVAC-PSO based optimal reactive power dispatch for reactive power cost allocation under deregulated environment. In IEEE Power & Energy Society (PES) General Meeting ‘09, Calgary, pp 1–9Google Scholar
  2. 2.
    Adankon MM, Cheriet M (2010) Genetic algorithm–based training for semi-supervised SVM. Neural Comput Appl 19:1197–1206. doi: 10.1007/s00521-010-0358-8 CrossRefGoogle Scholar
  3. 3.
    Anto S, Chandramathi S, Aishwarya S (2016) An expert system based on LS-SVM and simulated annealing for the diagnosis of diabetes disease. Int J Inf Commun Technol 9:88–100Google Scholar
  4. 4.
    Bhullar PS, Dhami JK (2016) PSO-TVAC-based economic load dispatch with valve-point loading. In Proceedings of Fifth International Conference on Soft Computing for Problem Solving ‘16, Singapore, pp 823–832Google Scholar
  5. 5.
    Bouchlaghem R, Elkhelifi A, Faiz R (2015) SVM based approach for opinion classification in Arabic written tweets. In IEEE/ACS 12th International Conference of Computer Systems and Applications ‘15, Marrakech, pp 1–4Google Scholar
  6. 6.
    Chen HL, Yang B, Liu J, Liu DY (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38:9014–9022CrossRefGoogle Scholar
  7. 7.
    Chen HL, Yang B, Wang S, Wang G, Dy L, Hz L, Wb L (2014) Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy. Appl Math Comput 239:180–197. doi: 10.1016/j.amc.2014.04.039 MathSciNetGoogle Scholar
  8. 8.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297MATHGoogle Scholar
  9. 9.
    Dai H, Zhao G, Lu J, Dai S (2014) Comment and improvement on “a new fruit fly optimization algorithm: taking the financial distress model as an example”. Knowl Based Syst 59:159–160CrossRefGoogle Scholar
  10. 10.
    Del Valle Y, Venayagamoorthy GK, Mohagheghi S (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195. doi: 10.1109/TEVC.2007.896686 CrossRefGoogle Scholar
  11. 11.
    Devroye L, Györfi L, Lugosi G (1996) Vapnik-Chervonenkis theory. In: A probabilistic theory of pattern recognition. Springer, New York, pp. 187–213CrossRefGoogle Scholar
  12. 12.
    Eggermont J, Kok JN, Kosters WA Genetic programming for data classification: partitioning the search space. In Proceedings of the 2004 ACM Symposium on Applied Computing ‘04, Nicosia, pp 1001–1005Google Scholar
  13. 13.
    Hosseini Bamakan SM, Wang H, Yingjie T, Shi Y (2016) An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing 199:90–102. doi: 10.1016/j.neucom.2016.03.031 CrossRefGoogle Scholar
  14. 14.
    Hsieh CC, Hsih MH, Jiang MK, Cheng YM, Liang EH (2015) Effective semantic features for facial expressions recognition using SVM. Multimedia Tools Appl 75:6663–6682CrossRefGoogle Scholar
  15. 15.
    Jordehi AR, Jasni J (2015) Particle swarm optimisation for discrete optimisation problems: a review. Artif Intell Rev 43:243–258CrossRefGoogle Scholar
  16. 16.
    Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In IEEE International Conference On Systems, Man, and Cybernetics, Computational Cybernetics and Simulation’97, Orlando, pp 4104–4108Google Scholar
  17. 17.
    Khare A, Rangnekar S (2013) A review of particle swarm optimization and its applications in solar photovoltaic system. Appl Soft Comput 13:2997–3006CrossRefGoogle Scholar
  18. 18.
    Li W, Xing X, Liu F, Zhang Y (2014) Application of improved grid search algorithm on SVM for classification of tumor gene. Int J Multimed Ubiquitous Eng 9:181–188. doi: 10.14257/ijmue.2014.9.11.18 CrossRefGoogle Scholar
  19. 19.
    Little MA, McSharry PE, Hunter EJ, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans Biomed Eng 56(4):1015–1022CrossRefGoogle Scholar
  20. 20.
    Löpprich M, Krauss F, Ganzinger M, Senghas K, Riezler S, Knaup P (2016) Automated classification of selected data elements from free-text diagnostic reports for clinical research. Methods Inf Med 55:373–380. doi: 10.3414/ME15-02-0019 CrossRefGoogle Scholar
  21. 21.
    Lu C, Zhu Z, Gu X (2014) An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method. J Med Syst 38:97–97. doi: 10.1007/s10916-014-0097-y CrossRefGoogle Scholar
  22. 22.
    Lubaib P, Muneer KVA (2016) The heart defect analysis based on PCG signals using pattern recognition techniques. Procedia Technol 24:1024–1031. doi: 10.1016/j.protcy.2016.05.225 CrossRefGoogle Scholar
  23. 23.
    Murugavel ASM, Ramakrishnan S (2014) Optimal feature selection using PSO with SVM for epileptic EEG classification. Int J Biomed Eng Technol 16:343–358. doi: 10.1504/IJBET.2014.066229 CrossRefGoogle Scholar
  24. 24.
    Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74CrossRefGoogle Scholar
  25. 25.
    Selvathi D, Emala T (2016) MRI brain pattern analysis for detection of Alzheimer’s disease using random forest classifier. Intell Decis Technol 1–10. doi: 10.3233/IDT-160260D
  26. 26.
    Shen L, Chen H, Kang W, Gu H, Zhang B, Ge T (2015) Fruit fly optimization algorithm based SVM classifier for efficient detection of Parkinson’s disease. In International Conference on Swarm Intelligence ‘15, Beijing, pp 98–106Google Scholar
  27. 27.
    Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 96:61–75. doi: 10.1016/j.knosys.2016.01.002 CrossRefGoogle Scholar
  28. 28.
    Street WN, Wolberg WH, Mangasarian OL (1993) Nuclear feature extraction for breast tumor diagnosis. In International Symposium on Electronic Imaging: Science and Technology ‘93, San Jose, pp 861–870Google Scholar
  29. 29.
    Varshney S, Srivastava L, Pandit M (2011) Optimal location and sizing of STATCOM for voltage security enhancement using PSO-TVAC. In IEEE International Conference on Power and Energy Systems ‘11, Chennai, pp 1–6Google Scholar
  30. 30.
    Vijayan A, Kareem S, Kizhakkethottam JJ (2016) Face recognition across gender transformation using SVM classifier. Procedia Technol 24:1366–1373CrossRefGoogle Scholar
  31. 31.
    Wang FS, Chen LH (2014) Particle swarm optimization (PSO). Appl Mech Mater 556-562:3965–3971CrossRefGoogle Scholar
  32. 32.
    Wang L, Liu R, Liu S (2016) An effective and efficient fruit fly optimization algorithm with level probability policy and its applications. Knowl Based Syst 97:158–174. doi: 10.1016/j.knosys.2016.01.006 CrossRefGoogle Scholar
  33. 33.
    Weronika Piątkowska MJ, Martyna J, Nowak L, Przystalski K (2011) A decision support system based on the semantic analysis of melanoma images using Multi-elitist PSO and SVM/machine learning and data mining in pattern recognition. In International Workshop on Machine Learning and Data Mining in Pattern Recognition ‘11, New York, pp 362–374Google Scholar
  34. 34.
    Wolberg WH, Mangasarian OL (1990) Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc Nalt Acad Sci USA 87:9193–9196CrossRefMATHGoogle Scholar
  35. 35.
    Zainuddin N, Selamat A, Ibrahim R (2016) Twitter feature selection and classification using support vector machine for aspect-based sentiment analysis. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems ‘16, Morioka, pp 269–279Google Scholar
  36. 36.
    Zheng XL, Wang L, Wang SY (2014) A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowl Based Syst 57:95–103MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengDuChina

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