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Preventing the Impact of Marital Dissolutions in Children by Regression Techniques

  • Nuria Rico
  • Alberto Guillén
  • Carlos Tovar
  • José F. Guillén
Part of the Communications in Computer and Information Science book series (CCIS, volume 221)

Abstract

The process of marital dissolution is a crisis that affects both the couple and their offspring. Many studies have shown how children involved in a marital dissolution could present less adaptation abilities as well as less healthy live habits. The longer the process, the more serious become these problems. Therefore, to be able to take preventive actions would be quite useful towards minimizing the dissolution process impact. This paper aims at supporting the decision of doctors when deciding about a possible treatment to children involved in a dissolution process studying the extension of time that the dissolution procces spend.

Classical statistical techniques as well as latest machine learning algorithms will be applied in order to predict how long the dissolution might take and which parameters could be the most significant. The information used in this study comes from the Spanish government monitorization of the dissolutions during the last years.

Keywords

Discriminant Analysis Radial Basis Function Dissolution Process Radial Basis Function Neural Network Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7(2), 179–188 (1936), http://hdl.handle.net/2440/15227, doi:10.1111/j.1469-1809.1936.tb02137.x (retrieved 2009-05-09)CrossRefGoogle Scholar
  2. 2.
    McLachlan, G.J.: Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience, Hoboken (2004) ISBN 0471691151. MR1190469zbMATHGoogle Scholar
  3. 3.
    Anda, R.F., Butchart, A., Felitti, V.J., Brown, D.W.: Building a Framework for Global Surveillance of the Public Health Implications of Adverse Childhood Experiences. American Journal of Preventive Medicine 39(1), 93–98 (2010)CrossRefGoogle Scholar
  4. 4.
    Chartiera, M.J., Walkerb, J.R., Naimark, B.: Separate and cumulative effects of adverse childhood experiences in predicting adult health and health care utilization. Child Abuse & Neglect 34, 454–464 (2010), doi:10.1016/j.chiabu.2009.09.020CrossRefGoogle Scholar
  5. 5.
    Due, P., Krølner, R., Rasmussen, M., Andersen, A., Damsgaard, M.T., Graham, H., Holstein, B.E.: Pathways and mechanisms in adolescence contribute to adult health inequalities. Scandinavian Journal of Public Health 39, 62 (2011), doi:10.1177/1403494810395989CrossRefGoogle Scholar
  6. 6.
    Benach, J.: Los determinantes demográficos y sociales de la salud. Gaceta Sanitaria 18(supl. 1), 7 (2004)CrossRefGoogle Scholar
  7. 7.
    Linj, B.G., Phelan, J.: Social conditions as fundamental causes of diseases. Journal of Health and Social Bahavior 24, 80–94 (1995)CrossRefGoogle Scholar
  8. 8.
    Borrell, C., Artazcoz, L.: Las políticas para disminuir las desigualdades en salud. Gaceta Sanitaria 22(5), 465–473 (2008)CrossRefGoogle Scholar
  9. 9.
    Ebrahim, S., Wannamethee, G., McCallum, A., Walker, M., Shaper, A.G.: Marital status, change in marital status, and mortality in middle-aged british men. American Journal of Epidemiology 142, 834–842 (1995)Google Scholar
  10. 10.
    Ben-Shlomo, Y., Davey Smith, G., Shipley, M., Marmot, M.G.: Magnitude and causes of motality differences between married and unmarried men. Journal of Epidemiology and Community Health 47, 200–205 (1993)CrossRefGoogle Scholar
  11. 11.
    Tucker, J.S., Friedman, H.S., Wingard, D.L., Schwartz, J.: Marital history at midlife as a predictor of longevity: alternative explanations to the protective effect of marriage. Health Psychol. 15, 94–101 (1996)CrossRefGoogle Scholar
  12. 12.
    Lund, R., Holstein, B.E., Osler, M.: Marital history from age 15 to 40 years and subsequent 10-year mortality: a longitudinal study of Danish males born in 1953. International Journal of Epidemiology 33, 389–397 (2004)CrossRefGoogle Scholar
  13. 13.
    Kposowa, A.J.: Marital status and suicide in the National Longitudinal Mortality Study. J. Epidemiol. Community Health 54, 254–261 (2000)CrossRefGoogle Scholar
  14. 14.
    Smith, J.C., Mercy, J.A., Conn, J.M.: Marital status and the risk of suicide. Am. J. Public Health 78, 78–80 (1988)CrossRefGoogle Scholar
  15. 15.
    Cutright, P., Stack, S., Fernquist, R.: Marital status integration, suicide disapproval, and societal integration as explanations of marital status differences in female age-specific suicide rates. Suicide Life Threat Behav. 37(6), 715–724 (2007)CrossRefGoogle Scholar
  16. 16.
    Manzoli, L., Villari, P., Pirone, G.M., Boccia, A.: Marital status and mortality in the elderly: A systematic review and meta-analysis. Social Science & Medicine 64, 77–94 (2007)CrossRefGoogle Scholar
  17. 17.
    Blanchard, L.T., Gurka, M.J., Blackman, J.A.: Emotional, developmental, and behavioral health of american children and their families: A report from the 2003 National Survey of Children’s Health. Pediatrics 117, e1202–e1212 (2006)CrossRefGoogle Scholar
  18. 18.
    González, J., Rojas, I., Ortega, J., Pomares, H., Fernández, F.J., Díaz, A.: Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation. IEEE Transactions on Neural Networks 14(6), 1478–1495 (2003)CrossRefGoogle Scholar
  19. 19.
    Guillén, A., González, J., Rojas, I., Pomares, H., Herrera, L.J., Valenzuela, O., Rojas, F.: Output Value-Based Initialization For Radial Basis Function Neural Networks. Neural Processing Letters (June 2007), doi:10.1007/s11063-007-9039-8Google Scholar
  20. 20.
    Guillén, A., Pomares, H., González, J., Rojas, I., Valenzuela, O., Prieto, B.: Parallel multiobjective memetic rbfnns design and feature selection for function approximation problems. Neurocomputing 72(16-18), 3541–3555 (2009)CrossRefGoogle Scholar
  21. 21.
    Park, J., Sandberg, J.W.: Universal approximation using radial basis functions network. Neural Computation 3, 246–257 (1991)CrossRefGoogle Scholar
  22. 22.
    González, J., Rojas, I., Ortega, J., Pomares, H., Fernández, F.J., Díaz, A.: Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation. IEEE Transactions on Neural Networks 14(6), 1478–1495 (2003)CrossRefGoogle Scholar
  23. 23.
    Guillén, A., González, J., Rojas, I., Pomares, H., Herrera, L.J., Valen-zuela, O., Prieto, A.: Improving Clustering Technique for Functional Approximation Problem Using Fuzzy Logic: ICFA algorithm. Neurocomputing (June 2007), doi:10.1016/j.neucom.2006.06.017Google Scholar
  24. 24.
    Guilléen, A., González, J., Rojas, I., Pomares, H., Herrera, L.J., Valenzuela, O., Rojas, F.: Studying Possibility in a Clustering for Function Approximation Algorithm. Neural Computing & Applications (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nuria Rico
    • 1
  • Alberto Guillén
    • 2
  • Carlos Tovar
    • 3
  • José F. Guillén
    • 3
  1. 1.Department of Statistics and Operational ResearchUniversidad de GranadaSpain
  2. 2.Department of Computer Architecture and Computer TechnologyUniversidad de GranadaSpain
  3. 3.Department of Preventive MedicineUniversidad de GranadaSpain

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