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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2018: Hybrid Artificial Intelligent Systems pp 547–557Cite as

Classification of Prostate Cancer Patients and Healthy Individuals by Means of a Hybrid Algorithm Combing SVM and Evolutionary Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10870))

Abstract

This research presents a new hybrid algorithm able to select a set of features that makes it possible to classify healthy individuals and those affected by prostate cancer.

In this research the feature selection is performed with the help of evolutionary algorithms. This kind of algorithms, have proven in previous researches their ability for obtaining solutions for optimization problems in very different fields. In this study, a hybrid algorithm based on evolutionary methods and support vector machine is developed for the selection of optimal feature subsets for the classification of data sets. The results of the algorithm using a reduced data set demonstrates the performance of the method when compared with non-hybrid methodologies.

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References

  1. Sánchez, M.J., Payer, T., De Angelis, R., Larrañaga, N., Capocaccia, R., Martinez, C.: CIBERESP working group. Cancer incidence and mortality in Spain: estimates and projections for the period 1981-2012. Ann. Oncol. 21(Suppl. 3), iii30–iii36 (2010)

    Google Scholar 

  2. Larrañaga, N., Galceran, J., Ardanaz, E., Franch, P., Navarro, C., Sánchez, M.J., Pastor-Barriuso, R.: Prostate cancer working group. Prostate cancer incidence trends in Spain before and during the prostate-specific antigen era: impact on mortality. Ann. Oncol. 21(Suppl. 3), iii83–iii89 (2010). https://doi.org/10.1093/annonc/mdq087

    Article  Google Scholar 

  3. Discacciati, A., Wolk, A.: Lifestyle and dietary factors in prostate cancer prevention. Recent Results Cancer Res. 202, 27–37 (2014). https://doi.org/10.1007/978-3-642-45195-9_3

    Article  Google Scholar 

  4. Cuzick, J., Thorat, M.A., Andriole, G., Brawley, O.W., Brown, P.H., Culig, Z., Eeles, R.A., Ford, L.G., Hamdy, F.C., Holmberg, L., Ilic, D., Key, T.J., La Vecchia, C., Lilja, H., Marberger, M., Meyskens, F.L., Minasian, L.M., Parker, C., Parnes, H.L., Perner, S., Rittenhouse, H., Schalken, J., Schmid, H.P., Schmitz-Dräger, B.J., Schröder, F.H., Stenzl, A., Tombal, B., Wilt, T.J., Wolk, A.: Prevention and early detection of prostate cancer. Lancet Oncol. 15(11), e484–e492 (2014). https://doi.org/10.1016/S1470-2045(14)70211-6

    Article  Google Scholar 

  5. Er, V., Lane, J.A., Martin, R.M., Emmett, P., Gilbert, R., Avery, K.N., Walsh, E., Donovan, J.L., Neal, D.E., Hamdy, F.C., Jeffreys, M.: Adherence to dietary and lifestyle recommendations and prostate cancer risk in the prostate testing for cancer and treatment (ProtecT) trial. Cancer Epidemiol. Biomark. Prev. 23(10), 2066–2077 (2014)

    Article  Google Scholar 

  6. Al Olama, A.A., Kote-Jarai, Z., Berndt, S.I., Conti, D.V., Schumacher, F., Han, Y., et al.: A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 46(10), 1103–1109 (2014)

    Article  Google Scholar 

  7. Ghasemi, M., Nabipour, I., Omrani, A., Alipour, Z., Assadi, M.: Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. Am. J. Nucl. Med. Mol. Imaging 6(6), 310–327 (2016)

    Google Scholar 

  8. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2010)

    MATH  Google Scholar 

  9. Artime Ríos, E.M., Seguí Crespo, M.M., Suarez Sánchez, A., Suárez Gómez, S.L., Sánchez Lasheras, F.: Genetic algorithm based on support vector machines for computer vision syndrome classification. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds.) SOCO/CISIS/ICEUTE 2017. AISC, vol. 649, pp. 381–390. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67180-2_37

    Chapter  Google Scholar 

  10. Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fith Annual Workshop on Computation Learning Theory, pp. 144–152 (1992)

    Google Scholar 

  11. Nieto, P.J.G., Lasheras, F.S., García-Gonzalo, E., de Cos Juez, F.J.: PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: a case study. Sci. Total Environ. 621, 753–761 (2018)

    Article  Google Scholar 

  12. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  13. Bradley, P.S., Mangasarian, O.L., Shavlik, J.: Feature selection via concave minimization and support vector machines. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), pp. 82–90. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  14. Rosado, P., Lequerica-Fernández, P., Villallaín, L., Peña, I., Sánchez Lasheras, F.: Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines. Expert Syst. Appl. 40(12), 4770–4776 (2013)

    Article  Google Scholar 

  15. Vilán, J.A.V., Fernández, J.R.A., Nieto, P.J.G., Lasheras, F.S., de Cos Juez, F.J.: Support vector machines and multilayer perceptron networks used to evaluate the cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain). Water Resour. Manag. 27(9), 3457–3476 (2013)

    Article  Google Scholar 

  16. Sánchez, A.S., Fernández, P.R., Lasheras, F.S., de Cos Juez, F.J., Nieto, P.J.G.: Prediction of work-related accidents according to working conditions using support vector machines. Appl. Math. Comput. 218(7), 3539–3552 (2011)

    MathSciNet  Google Scholar 

  17. Álvarez Antón, J.C., Nieto, P.J.G., de Cos Juez, F.J., Lasheras, F.S., Vega, M.G.: Battery state-of-charge estimator using the SVM technique. Appl. Math. Model. 37(9), 6244–6253 (2013)

    Article  Google Scholar 

  18. Fogel, L., Evans, M., Walsh, M.: Artificial Intelligence through Simulated Evolution. Wiley, New York (1966). Evolutionary Programming

    MATH  Google Scholar 

  19. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  20. Galán, C.O., Lasheras, F.S., de Cos Juez, F.J., Sánchez, A.B.: Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions. J. Comput. Appl. Math. 311, 704–717 (2017)

    Article  MathSciNet  Google Scholar 

  21. Castaño-Vinyals, G., Aragonés, N., Pérez-Gómez, B., Martín, V., Llorca, J., Moreno, V., Altzibar, J.M., Ardanaz, E., De Sanjosé, S., Jiménez-Moleón, J.J., et al.: Population-based multicase-control study in common tumors in Spain (MCC-Spain): rationale and study design. Gac. Sanit. 29(4), 308–315 (2015)

    Article  Google Scholar 

  22. Nieto, P.J.G., García-Gonzalo, E., Lasheras, F.S., de Cos Juez, F.J.: Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliab. Eng. Syst. Saf. 138, 219–231 (2015)

    Article  Google Scholar 

  23. Allott, E.H., Masko, E.M., Freedland, S.J.: Obesity and prostate cancer: weighing the evidence. Eur. Urol. 63(5), 800–809 (2013). https://doi.org/10.1016/j.eururo.2012.11.013. Epub 2012 Nov 15. Review

    Article  Google Scholar 

  24. Rodriguez, C., McCullough, M.L., Mondul, A.M., Jacobs, E.J., Fakhrabadi- Shokoohi, D., Giovannucci, E.L., et al.: Calcium, dairy products, and risk of prostate cancer in a prospective cohort of United States men. Cancer Epidemiol. Biomark. Prev. 12, 597–603 (2003)

    Google Scholar 

  25. Gallus, S., Scotti, L., Negri, E., Talamini, R., Franceschi, S., Montella, M., Giacosa, A., Dal Maso, L., La Vecchia, C.: Artificial sweeteners and cancer risk in a network of case-control studies. Ann. Oncol. 18(1), 40–44 (2007)

    Article  Google Scholar 

  26. Xiudong, X., Ying, W., Xiaoli, L., Ying, L., Jianzhong, Z.: Soymilk residue (okara) as a natural immobilization carrier for Lactobacillus plantarum cells enhances soymilk fermentation, glucosidic isoflavone bioconversion, and cell survival under simulated gastric and intestinal conditions. PeerJ 4, e2701 (2016)

    Article  Google Scholar 

  27. Diallo, A., Deschasaux, M., Galan, P., et al.: Associations between fruit, vegetable and legume intakes and prostate cancer risk: results from the prospective Supplementation en Vitamines et Mineraux Anti-oxydants (SU.VI.MAX) cohort. Br. J. Nutr. 115, 1579–1585 (2016)

    Article  Google Scholar 

  28. Lin, P.H., Aronson, W., Freedland, S.J.: Nutrition, dietary interventions and prostate cancer: the latest evidence. BMC Med. 13, 3 (2015)

    Article  Google Scholar 

  29. Stangelberger, A., Waldert, M., Djavan, B.: Prostate cancer in elderly men. Rev. Urol. 10(2), 111–119 (2008)

    Google Scholar 

  30. Davies, N.M., Gaunt, T.R., Lewis, S.J., Holly, J., Donovan, J.L., Hamdy, F.C., Kemp, J.P., Eeles, R., Easton, D., Kote-Jarai, Z., Al Olama, A.A., Benlloch, S., Muir, K., Giles, G.G., Wiklund, F., Gronberg, H., Haiman, C.A., Schleutker, J., Nordestgaard, B.G., Travis, R.C., Neal, D., Pashayan, N., Khaw, K.T., Stanford, J.L., Blot, W.J., Thibodeau, S., Maier, C., Kibel, A.S., Cybulski, C., Cannon-Albright, L., Brenner, H., Park, J., Kaneva, R., Batra, J., Teixeira, M.R., Pandha, H., PRACTICAL consortium, Lathrop, M., Smith, G.D., Martin, R.M.: The effects of height and BMI on prostate cancer incidence and mortality: a Mendelian randomization study in 20,848 cases and 20,214 controls from the PRACTICAL consortium. Cancer Causes Control 26(11), 1603–1616 (2015)

    Article  Google Scholar 

  31. Calle, E.E., Kaaks, R.: Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat. Rev. Cancer 4, 579–591 (2004)

    Article  Google Scholar 

  32. Keto, C.J., Aronson, W.J., Terris, M.K., Presti, J.C., Kane, C.J., Amling, C.L., Freedland, S.J.: Obesity is associated with castration-resistant disease and metastasis in men treated with androgen deprivation therapy after radical prostatectomy: results from the SEARCH database. BJU Int. 110(4), 492–498 (2012)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Anesthesiology and Resuscitation Service, Hospital Carmen y Severo Ochoa, Cangas del Narcea, Spain

    Juan Enrique Sánchez Lasheras

  2. Department of Mathematics, University of Oviedo, Oviedo, Spain

    Fernando Sánchez Lasheras

  3. Centro Investigación Biomédica en Red Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

    Carmen González Donquiles & Adonina Tardón

  4. Universitary Institute of Oncology of Asturias (IUOPA), University of Oviedo, Oviedo, Spain

    Carmen González Donquiles & Adonina Tardón

  5. Research Group of Gene-Environment-Health Interactions, Biomedicine Institute (IBIOMED), University of Leon, León, Spain

    Carmen González Donquiles

  6. Consortium for Biomedical Research in Epidemiology & Public Health (CIBERESP), Carlos III Institute of Health, Madrid, Spain

    Gemma Castaño Vynals & Beatriz Pérez Gómez

  7. ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain

    Gemma Castaño Vynals

  8. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Gemma Castaño Vynals

  9. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain

    Gemma Castaño Vynals

  10. Cancer Epidemiology Unit, National Centre for Epidemiology, Carlos III Institute of Health, Madrid, Spain

    Beatriz Pérez Gómez

  11. Cancer Epidemiology Research Group, Oncology and Hematology Area, IIS Puerta de Hierro (IDIPHIM), Madrid, Spain

    Beatriz Pérez Gómez

  12. Universidad de Cantabria – IDIVAL, Santander, Spain

    Camilo Palazuelos

  13. Valencia Cancer and Public Health Area, FISABIO - Public Health, Valencia, Spain

    Dolors Sala

  14. General Directorate Public Health, Valencian Community, Valencia, Spain

    Dolors Sala

  15. Department of Mines Prospecting and Exploitation, University of Oviedo, Oviedo, Spain

    Francisco Javier de Cos Juez

Authors
  1. Juan Enrique Sánchez Lasheras
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  2. Fernando Sánchez Lasheras
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  3. Carmen González Donquiles
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  4. Adonina Tardón
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  5. Gemma Castaño Vynals
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  6. Beatriz Pérez Gómez
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  7. Camilo Palazuelos
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  8. Dolors Sala
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  9. Francisco Javier de Cos Juez
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Corresponding author

Correspondence to Fernando Sánchez Lasheras .

Editor information

Editors and Affiliations

  1. Department of Mine Operating and Prospection, University of Oviedo, Oviedo, Spain

    Francisco Javier de Cos Juez

  2. Department of Computer Science, University of Oviedo, Oviedo, Spain

    José Ramón Villar

  3. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    José António Sáez

  7. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Lasheras, J.E.S. et al. (2018). Classification of Prostate Cancer Patients and Healthy Individuals by Means of a Hybrid Algorithm Combing SVM and Evolutionary Algorithms. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_46

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  • DOI: https://doi.org/10.1007/978-3-319-92639-1_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92638-4

  • Online ISBN: 978-3-319-92639-1

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