Skip to main content
SpringerLink
Log in

Bronchopulmonary Dysplasia Prediction Using Support Vector Machine and Logit Regression

  • Conference paper
Information Technologies in Biomedicine, Volume 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 284))

Abstract

The paper presents BPD (Bronchopulmonary Dysplasia) prediction for extremely premature infants after their first week of life. SVM (Support Vector Machine) and LR (Logit Regression) are used as classifiers. Data was collected thanks to the Neonatal Intensive Care Unit of The Department of Pediatrics at Jagiellonian University Medical College and includes 109 patients with birth weight less than or equal to 1500g. Fourteen different risk factor parameters were considered and all 214 combinations were analyzed. Classifier based on six feature LR model provides accuracy up to 82%, while SVM one turns out to be generally much worse, providing in best case scenario 80% of accuracy. In addition, the article discusses the influence of the model parameters selection on prediction quality.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Horbar, J.D., Badger, G.J., Carpenter, J.H., Fanaroff, A.A., Kilpatrick, S., LaCorte, M., Phibbs, R., Soll, R.F.: Members of the Vermont Oxford Network. Trends in mortality and morbidity for very low birth weight infants, 1991-1999. Pediatrics 110, 143–151 (2002)

    Article  Google Scholar 

  2. Stoll, B.J., Hansen, N.I., Bell, E.F., Shankaran, S., Laptook, A.R., Walsh, M.C., Hale, E.C., Newman, N.S., Schibler, K., Carlo, W.A., et al.: Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. Neonatal outcomes of extremely pre- term infants from the NICHD Neonatal Research Network. Pediatrics 126, 443–456 (2010)

    Article  Google Scholar 

  3. Jobe, A.H.: The new bronchopulmonary dysplasia. Current Opinion in Pediatrics 23(2), 167 (2011)

    Article  Google Scholar 

  4. Groothuis, J.R., Makari, D.: Definition and outpatient management of the very low-birth-weight infant with bronchopulmonary dysplasia. Advances in Therapy 29(4), 297–311 (2012)

    Article  Google Scholar 

  5. Walsh, M., et al.: Summary proceedings from the bronchopulmonary dysplasia group. Pediatrics 117(3), S52–S56 (2006)

    Google Scholar 

  6. Tapia, J.L., Agost, D., Alegria, A., Standen, J., Escobar, M., Grandi, C., et al.: Bronchopulmonary dysplasia: incidence, risk factors and resource utilization in a population of South American very low birth weight infants. Journal de Pediatria (Rio J) 82(1), 15–20 (2006)

    Google Scholar 

  7. Farstad, T., Bratlid, D., Medbø, S., Markestad, T.: Bronchopulmonary dysplasia–prevalence, severity and predictive factors in a national cohort of extremely premature infants. Acta Paediatrica 100(1), 53–58 (2011)

    Article  Google Scholar 

  8. Ryan, S.W., Nycyk, J., Shaw, B.N.: Prediction of chronic neonatal lung disease on day 4 of life. Eur. J. Pediatr. 155, 668–671 (1996)

    Article  Google Scholar 

  9. Subhedar, N.V., Hamdan, A.H., Ryan, S.W., Shaw, N.J.: Pulmonary artery pressure: early predictor of chronic lung disease in preterm infants. Arch. Dis. Child. Fetal. Neonatal Ed. 78, F20–F24 (1998)

    Google Scholar 

  10. Romagnoli, C., Zecca, E., Tortorolo, L., Vento, G., Tortorolo, G.: A scoring system to predict the evolution of respiratory distress syndrome into chronic lung disease in preterm infants. Intensive Care Med. 24, 476–480 (1998)

    Article  Google Scholar 

  11. Toce, S.S., Farrell, P.M., Leavitt, L.A., Samuels, D.P., Edwards, D.K.: Clinical and roentgenographic scoring systems for assessing bronchopulmonary dysplasia. Am. J. Dis. Child. 138, 581–585 (1984)

    Google Scholar 

  12. Corcoran, J.D., Patterson, C.C., Thomas, P.S., Halliday, H.L.: Reduction in the risk of bronchopulmonary dysplasia from 1980-1990: results of a multivariate logistic regression analysis. Eur. J. Pediatr. 152, 677–681 (1993)

    Article  Google Scholar 

  13. Noack, G., Mortensson, W., Robertson, B., Nilsson, R.: Correlations between radiological and cytological findings in early development of bronchopulmonary dysplasia. Eur. J. Pediatr. 152, 1024–1029 (1993)

    Article  Google Scholar 

  14. Yuksel, B., Greenough, A., Karani, J.: Prediction of chronic lung disease from the chest radiograph appearance at seven days of age. Acta Paediatr. 82, 944–947 (1993)

    Article  Google Scholar 

  15. Bhutani, V.K., Abbasi, S.: Relative likelihood of bronchopulmonary dysplasia based on pulmonary mechanics measured in preterm neonates during the first week of life. J. Pediatr. 120, 605–613 (1992)

    Article  Google Scholar 

  16. Kim, Y.D., Kim, E.A., Kim, K.S., Pi, S.Y., Kang, W.: Scoring method for early prediction of neonatal chronic lung disease using modified respiratory parameters. J. Korean. Med. Sci. 20, 397–401 (2005)

    Article  Google Scholar 

  17. Bhering, C.A., Mochdece, C.C., Moreira, M.E., Rocco, J.R., Sant’Anna, G.M.: Bronchopulmonary dysplasia prediction model for 7-day-old infants. J. Pediatr. (Rio J) 83, 163–170 (2007)

    Google Scholar 

  18. Rojas, M.A., Gonzalez, A., Bancalari, E., Claure, N., Poole, C., Silva-Neto, G.: Changing trends in the epidemiology and pathogenesis of neonatal chronic lung disease. J. Pediatr. 126, 605–610 (1995)

    Article  Google Scholar 

  19. Marshall, D.D., Kotelchuck, M., Young, T.E., Bose, C.L., Kruyer, L., O’Shea, T.M.: Risk factors for chronic lung disease in the surfactant era: a North Carolina population-based study of very low birth weight infants. North Carolina Neonatologists Association. Pediatrics 104, 1345–1350 (1999)

    Google Scholar 

  20. Oh, W., Poindexter, B.B., Perritt, R., Lemons, J.A., Bauer, C.R., Ehrenkranz, R.A., Stoll, B.J., Poole, K., Wright, L.L.: Neonatal Research Network. Association between fluid intake and weight loss during the first ten days of life and risk of bronchopulmonary dysplasia in extremely low birth weight infants. J. Pediatr. 147, 786–790 (2005)

    Article  Google Scholar 

  21. Ambalavanan, N., Van Meurs, K.P., Perritt, R., Carlo, W.A., Ehrenkranz, R.A., Stevenson, D.K., Lemons, J.A., Poole, W.K., Higgins, R.D.: NICHD Neo- natal Research Network, Bethesda, MD. Predictors of death or bronchopulmonary dysplasia in preterm infants with respiratory failure. J. Perinatol. 28, 420–426 (2008)

    Article  Google Scholar 

  22. Gilbert, R., Keighley, J.: The arterial/alveolar oxygen tension ratio. An index of gas exchange applicable to varying inspired oxygen concentrations. Am. Rev. Respir. Dis. 109, 142–145 (1974)

    Google Scholar 

  23. Stoch, P.: Prediction of BronchoPulmonary Dysplasia in preterm neonates using statistical and artificial neural network tools (Thesis or Dissertation style) Ph.D. dissertation, AGH University of Science and Technology, Kraków, pp. 60–72 (2007) (in Polish)

    Google Scholar 

  24. Kuenzel, L.: Predicting and undestanding bronchopulmonary dysplasia in permature infants. Stanford Undergraduate Research Journal

    Google Scholar 

  25. Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, vol. 2, pp. 121–167. Kluwer Academic Publishers, Boston (1998)

    Google Scholar 

  26. Sosenko, I.R., Bancalari, E.: New Developments in the Pathogenesis and Prevention of Bronchopulmonary Dysplasia. The Newborn Lung: Neonatology Questions and Controversies: Expert Consult-Online and Print 217 (2012)

    Google Scholar 

  27. Cunha, G.S., Mezzacappa-Filho, F., Ribeiro, J.D.: Risk Factors for Bronchopulmonary Dysplasia in very Low Birth Weight Newborns Treated with Mechanical Ventilation in the First Week of Life. Journal of Tropical Pediatrics 51(6), 334–340 (2005)

    Article  Google Scholar 

  28. Jones, H.L.: Jacknife estimation of functions of stratum means. Biometrika 61(2), 343–348 (1974)

    MATH  MathSciNet  Google Scholar 

  29. Ali, Z., Schmidt, P., Dodd, J., Jeppesen, D.L.: Bronchopulmonary dysplasia: a review. Archives of Gynecology and Obstetrics, 1–9 (2013)

    Google Scholar 

  30. Laughon, M.M., et al.: Prediction of bronchopulmonary dysplasia by postnatal age in extremely premature infants. American Journal of Respiratory and Critical Care Medicine 183(12), 1715 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AGH University of Science and Technology, 30 Mickiewicza, 30-059, Kraków, Poland

    Marcin Ochab & Wiesław Wajs

Authors
  1. Marcin Ochab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wiesław Wajs
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcin Ochab .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Ewa Piętka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Ochab, M., Wajs, W. (2014). Bronchopulmonary Dysplasia Prediction Using Support Vector Machine and Logit Regression. In: Piętka, E., Kawa, J., Wieclawek, W. (eds) Information Technologies in Biomedicine, Volume 4. Advances in Intelligent Systems and Computing, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-319-06596-0_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06596-0_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06595-3

  • Online ISBN: 978-3-319-06596-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation