Jump to ContentJump to Main Navigation
Show Summary Details
More options …

International Journal of Nonlinear Sciences and Numerical Simulation

Editor-in-Chief: Birnir, Björn

Editorial Board: Armbruster, Dieter / Chen, Xi / Bessaih, Hakima / Chou, Tom / Grauer, Rainer / Marzocchella, Antonio / Rangarajan, Govindan / Trivisa, Konstantina / Weikard, Rudi

8 Issues per year

IMPACT FACTOR 2017: 1.162

CiteScore 2017: 1.41

SCImago Journal Rank (SJR) 2017: 0.382
Source Normalized Impact per Paper (SNIP) 2017: 0.636

Mathematical Citation Quotient (MCQ) 2017: 0.12

See all formats and pricing
More options …
Volume 19, Issue 6


An Automated Computer System Based on Genetic Algorithm and Fuzzy Systems for Lung Cancer Diagnosis

Abir Alharbi
Published Online: 2018-07-19 | DOI: https://doi.org/10.1515/ijnsns-2017-0048


An automated system for the diagnosis of lung cancer is proposed in this paper, the system is designed by combining two major methodologies, namely the fuzzy base systems and the evolutionary genetic algorithms (GAs), to be employed on lung cancer data to assist physicians in the early detection of lung cancers, and hence obtain an early automated diagnosis complementary to that by physicians. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized diagnosis systems that attain high classification performance, in fact, our best six rule system obtained a 97.5 % accuracy, with simple and well interpretive rules, with 93 % degree of confidence, and without the need for dimensionality reduction. The results on real data indicate that the proposed system is very effective in the diagnosis of lung cancer and can be used for clinical applications.

Keywords: fuzzy systems; genetic algorithms; optimization methods; lung cancer; computer-aided diagnosis (CAD)

JEL Classification: 68T01; 68T20; 03B52; 65Y10


  • [1]

    American Lung Association’s National Office, 55 W. Wacker Drive, Suite 1150, Chicago, IL 60601, http://www.lung.org/about-us/contact-us.html.

  • [2]

    H. Abe, H. MacMahon, J. Shiraishi, Q. Li, R. Engelmann and K. Doi, Computer-aided diagnosis in chest radiography, Seminars Ultrasound, CT MRI 25 (2004), 432–437.CrossrefGoogle Scholar

  • [3]

    M.L. Giger, Computerized analysis of images in the detection and diagnosis of breast cancer, Seminars Ultrasound CT MRI 25 (2004), 411–418.CrossrefGoogle Scholar

  • [4]

    F. Feng, Y. Wu, Y. Wu, G. Nie and R. Ni, The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer, J. Med. Syst., 36(5): (2012), 2973–80.Google Scholar

  • [5]

    K. Polat and S. Gunes, Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer, Expert Syst. Appl., 34 (2008), 214–221.Web of ScienceCrossrefGoogle Scholar

  • [6]

    E. Avci, A new expert system for diagnosis of lung cancer: GDALS_SVM, J. Med. Syst. 36 (3) (2011), 2005–2009.Google Scholar

  • [7]

    S.V. Destounis, P. Di Nitto, W. Logan-Young, E. Bonaccio, M.L. Zuley and K.M. Willison, Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience, Radiology 232 (2004), 578–584.CrossrefGoogle Scholar

  • [8]

    K. Doi, Current status and future potential of computer-aided diagnosis in medical imaging, Br. J. Rad., 78 (2005) No 1:S3-S19.Google Scholar

  • [9]

    K. Doi, Diagnostic imaging over the last 50 years: Research and development in medical imaging science and technology, Phys. Med. Biol. 51 (2006), 5–27.CrossrefGoogle Scholar

  • [10]

    M.L. Giger, K. Doi and H. MacMahon, Computerized detection of lung nodules in digital chest radiographs, Proc. SPIE 767 (1987), 384–386.CrossrefGoogle Scholar

  • [11]

    M.R. Daliri, Automated diagnosis of Alzheimer disease using the scale-invariant feature transforms in magnetic resonance images, J. Med. Syst., Springer US, 36 (2) (2012), 995–1000.Web of ScienceCrossrefGoogle Scholar

  • [12]

    M.R. Daliri, A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines images, J. Med. Syst., Springer US, 36 (2) (2012), 1001–1005.CrossrefGoogle Scholar

  • [13]

    M.R. Daliri, Combining extreme learning machines using support vector machines for breast tissue classification, Comput. Methods Biomech. Biomed. Engin. 18 (2) (2015), 185–91.Web of ScienceGoogle Scholar

  • [14]

    M.R. Daliri, Automatic diagnosis of neuro-degenerative diseases using gait dynamics, Meas. 45 (7) (2012), 1729–1734.CrossrefGoogle Scholar

  • [15]

    H. Karimi Rouzbahani and M.R. Daliri, diagnosis of parkinson’s disease in human using voice signals, BCN 2 (3) (2011), 12–20.Google Scholar

  • [16]

    A. Khorasani and M.R. Daliri, HMM for classification of Parkinson’s disease based on the raw gait data, M.R. J. Med. Syst. 38 (2014), 147.Web of ScienceCrossrefGoogle Scholar

  • [17]

    M.R. Daliri, Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis, Biomed. Tech./Biomed. Eng. 57 (5) (2014), 395–402.Web of ScienceGoogle Scholar

  • [18]

    A. Onan, A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer, Expert Syst. Appl. 42 (20) (2015), 6844–6852.CrossrefWeb of ScienceGoogle Scholar

  • [19]

    A. Onan, A stochastic gradient descent based SVM with Fuzzy-Rough feature selection and instance selection for breast cancer diagnosis, J. Med. Imaging Health Inform. 5 (6) (2015), 1233–1239.CrossrefWeb of ScienceGoogle Scholar

  • [20]

    A. Alharbi and F. Tchier, Using a Genetic-Fuzzy algorithm as a computer aided diagnosis tool on saudi arabian breast cancer database, Math. Biosci. 286 (April 2017), 39–48.Web of ScienceCrossrefGoogle Scholar

  • [21]

    O. Cordon, F. Herrera and M. Lozano, On the combination of fuzzy logic and evolutionary computation: A short review and bibliography, Fuzzy Evo. Comp., Kluwer (1997), 1, 33–56.Google Scholar

  • [22]

    H. Heider and T. Drabe, “Fuzzy system design with a cascaded genetic algorithm”. IEEE International Conference on Evolutionary Computation, 1997; pp. 585–588.Google Scholar

  • [23]

    M.A. Lee and H. Takagi, “Integrating design stages of fuzzy systems using genetic algorithms”, IEEE International Conference on Fuzzy Systems, 1993; pp. 612–617.Google Scholar

  • [24]

    C. Andres, P. Reyes and M. Sipper, A genetic-fuzzy approach to breast cancer diagnosis, Artif. Intell. Med. 17, Elsevier, (1999), 131–155.CrossrefGoogle Scholar

  • [25]

    C.J. Carmona, V. Ruiz-Rodado, M.J. Del Jesus, A. Weber, M. Grootveld, P. González and D. Elizondo, A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans, Inf. Sci. (Ny), 298 (2015), 180–197.Web of ScienceCrossrefGoogle Scholar

  • [26]

    T. Nguyen, A. Khosravi and D. Creighton, Classification of healthcare data using genetic fuzzy logic system and wavelets, Expert. Syst. Appl. 42 (4) (2015), 2184–2197.CrossrefWeb of ScienceGoogle Scholar

  • [27]

    J.R. Jang and C.T. Sun, Neuro-fuzzy modeling and control, Proc. IEEE, 83 (3) (1995), 378–406.CrossrefGoogle Scholar

  • [28]

    O.L. Mangasarian, W.N. Street and W.H. Wolberg, “Breast cancer diagnosis and prognosis via linear programming”, Mathematical Programming Technical Report, 1994; pp. 94–10.Google Scholar

  • [29]

    P. Vuorimaa, Fuzzy self-organizing map, Fuzzy Sets Syst. 66 (1994), 223–231.CrossrefGoogle Scholar

  • [30]

    R.R. Yager and D.P. Filev, Essentials of Fuzzy Modeling and Control, John Wiley, 1994.Google Scholar

  • [31]

    L.A. Zadeh, Fuzzy sets, Inf. Control. 8 (3) (1965), 338–353.CrossrefGoogle Scholar

  • [32]

    R.R. Yager and L.A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing, New York, Van Nostrand Reinhold, 1994.Google Scholar

  • [33]

    S. Muthukrishnan, GFS: Adaptive Genetic Fuzzy system for medical data classification B Dennis, Appl. Soft. Comput., Elsevier (2014), 25, 242–52.Google Scholar

  • [34]

    J.M. Mendel, Fuzzy logic systems for engineering: A tutorial, Proc. IEEE. 83 (3) (1995), 345–377.CrossrefGoogle Scholar

  • [35]

    H.L. Chen, C.C. Huang, X.G. Yu, X. Xu, X. Sun and G. Wang, An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach, Expert. Syst. Appl. 40 (2013), 263–271.Web of ScienceCrossrefGoogle Scholar

  • [36]

    M.F. Ganji and M.S. Abadeh, A fuzzy classification system based on ant colony optimization for diabetes disease diagnosis, Expert. Syst. Appl. 38 (2011), 14650–14659.Web of ScienceCrossrefGoogle Scholar

  • [37]

    B. Kovalerchuk, E. Triantaphyllou, J.F. Ruiz and J. Clayton, Fuzzy logic in computer-aided breast cancer diagnosis, Artif. Intell. Med. 11 (1) (1997), 75–85.CrossrefGoogle Scholar

  • [38]

    D.Y. Liu, H.L. Chen, B. Yang, L.N. Li and J. Liu, Design of an enhanced fuzzy k-nearest neighbor classifier based on computer aided diagnostic system for thyroid disease, J. Med. Syst. 36 (2012), 3243–3254.Web of ScienceCrossrefGoogle Scholar

  • [39]

    C. Shang and D. Barnes, Fuzzy-rough feature selection aided support vector machines for Mars image classification, Comput. Vis. Image Understanding 117 (2013), 202–213.CrossrefWeb of ScienceGoogle Scholar

  • [40]

    J.A. Rodger, A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings, Expert. Syst. Appl. 41 (2014), 1813–1829.Web of ScienceCrossrefGoogle Scholar

  • [41]

    J.A. Rodger, Application of a fuzzy feasibility Bayesian probabilistic estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets, Expert Syst. Appl. 41 (2014), 7005–7022.Web of ScienceCrossrefGoogle Scholar

  • [42]

    J.A. Rodger, Discovery of medical Big Data analytics: Improving the prediction of traumatic brain injury survival rates by datamining Patient Informatics Processing Software Hybrid Hadoop Hive, Inform. Med. Unlocked. 1 (2015) 17–2618.CrossrefGoogle Scholar

  • [43]

    Z. Michalewicz, Genetic Algorithms+Data Structures=Evolution Programs, 3rd, Springer-Verlag, Berlin Heidelberg, 1996.Google Scholar

  • [44]

    J.R. Koza, Genetic Programming, MIT Press, Cambridge, MA., 1992.Google Scholar

  • [45]

    F. Herrera, M. Lozano and J.L. Verdegay, Generating fuzzy rules from examples using genetic algorithms, Fuzzy Logic Soft Comput., World Scientific, (1995), 4, 11–20.CrossrefGoogle Scholar

  • [46]

    C.L. Karr, Genetic algorithms for fuzzy controllers, A. I. Expert, 6 (2) (1991), 26–33.Google Scholar

  • [47]

    C.J. Merz and P.M. Murphy, UCI machine learning repository Irvine, CA: University of California, Sch. Inf. Comput. Sci. (2010), http://archive.ics.uci.edu/ml.

  • [48]

    MATLAB Tool Box Guide Accessed Jan 2015 from http://www.mathworks.com/products/global-optimization/features.html#genetic-algorithm-solver.

About the article

Received: 2017-02-16

Accepted: 2018-07-07

Published Online: 2018-07-19

Published in Print: 2018-09-25

Citation Information: International Journal of Nonlinear Sciences and Numerical Simulation, Volume 19, Issue 6, Pages 583–594, ISSN (Online) 2191-0294, ISSN (Print) 1565-1339, DOI: https://doi.org/10.1515/ijnsns-2017-0048.

Export Citation

© 2018 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in