Determinants of Cesarean Section among Primiparas: A Comparison of Classification Methods
-
Saman MAROUFIZADEH
,
Payam AMINI
,
Mostafa HOSSEINI
,
Amir ALMASI-HASHIANI
,
Maryam MOHAMMADI
,
Behnaz NAVID
,
Reza OMANI-SAMANI
Abstract
Background: Over the last few decades, Cesarean section (CS) rates have increased significantly worldwide particularly in Iran. Classification methods including logistic regression (LR), random forest (RF) and artificial neural network (ANN) were used to identify factors related to CS among primipars.
Methods: This cross-sectional study included 2120 primipars who gave singleton birth in Tehran, Iran between 6 and 21 July 2015. To identify factor associated with CS, the classification methods were compared in terms of sensitivity, specificity, and accuracy.
Results: The CS rate was 72.1%. Mother’s age, SES, BMI, baby’s head circumference and infant weight were the most important determinant variables for CS as identified by the ANN method which had the highest accuracy (0.70). The association of RF predictions and observed values was 0.36 (kappa).
Conclusion: The ANN method had the best performance that classified CS delivery compared to the RF and LR methods. The ANN method might be used as an appropriate method for such data.
1. Mussarat N, Qurashi S, Roohi M (2013). Lower segment cesarean section (LSCS); indications and complications at teahcing hospital, Faisalabad. Professional Med J, 20(6): 916-923.
2. Betrán AP, Ye J, Moller A-B, Zhang J et al (2016). The Increasing Trend in Caesarean Section Rates: Global, Regional and National Estimates: 1990-2014. PLoS One, 11(2):e0148343.
3. Cunningham F, Leveno K, Bloom S, Hauth J, Rouse D, Spong C (2005). Williams Obstetrics, 25th ed. New York, NY, USA: McGraw-Hill.
4. Allen VM, O'Connell CM, Liston RM, Baskett TF (2003). Maternal morbidity associated with cesarean delivery without labor compared with spontaneous onset of labor at term. Obstet Gynecol, 102(3):477-482.
5. Häger RM, Daltveit AK, Hofoss D et al (2004). Complications of cesarean deliveries: rates and risk factors. Am J Obstet Gynecol, 190(2):428-434.
6. Hebert PR, Reed G, Entman SS, Mitchel Jr EF et al (1999). Serious maternal morbidity after childbirth: prolonged hospital stays and readmissions. Obstet Gynecol, 94(6):942-947.
7. Murphy D, Stirrat G, Heron J, Team AS (2002). The relationship between Caesarean section and subfertility in a population-based sample of 14 541 pregnancies. Hum Reprod, 17(7):1914-1917.
8. Owen J, Andrews WW (1994). Wound complications after cesarean sections. Clin Obstet Gynecol, 37(4):842-855.
9. Schuitemaker N, Roosmalen J, Dekker G et al (1997). Maternal mortality after cesarean section in The Netherlands. Acta Obstet Gynecol Scand, 76(4):332-334.
10. Levine EM, Ghai V, Barton JJ, Strom CM (2001). Mode of delivery and risk of respiratory diseases in newborns. Obstet Gynecol, 97(3):439-442.
11. Towner D, Castro MA, Eby-Wilkens E, Gilbert WM (1999). Effect of mode of delivery in nulliparous women on neonatal intracranial injury. N Engl J Med, 341(23):1709-1714.
12. Miri Farahani L, Abbasi Shavazi MJ (2012). Caesarean section change trends in iran and some demographic factors associated with them in the past three decades. J Fasa Univ Med Sci, 2(3):127-134.
13. Movahed M, Enayat H, Ghaffarinasab E et al (2012). Related factors to choose cesarean rather than normal delivery among Shirazian pregnant women. J Fasa Univ Med Sci, 2(2):78-83.
14. Jiawei H, Kamber M, Pei J (2012). Data mining: concepts and techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann.
15. Kantardzic M (2011). Data mining: concepts, models, methods, and algorithms, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons.
16. Hernandez-Suarez G, Sanabria M, Serrano M, Zabaleta J, Tenesa A (2013). TGFBR1 and TP53 SNPs interactions associated with colorectal cancer risk: Analysis of metabolic pathways using a Random Forest approach. Cancer Res, 73(8):4840-4840.
17. Milewski R, Milewska AJ, Więsak T, Morgan A (2013). Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment. Studies in Logic, Grammar and Rhetoric, 35(1):39-48.
18. Amini P, Maroufizadeh S, Hamidi O, Samani RO, Sepidarkish M (2016). Factors associated with macrosomia among singleton live-birth: A comparison between logistic regression, random forest and artificial neural network methods. Epidemiol Biostat Public Health, 13(4):e119851-e119859.
19. Breiman L (2001). Random forests. Machine Learning, 45(1):5-32.
20. Dreiseitl S, Ohno-Machado L (2002). Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 35(5):352-359.
21. Ayat S, Farahani HA, Aghamohamadi M et al (2013). A comparison of artificial neural networks learning algorithms in predicting tendency for suicide. Neural Comput Appl, 23(5):1381-1386.
22. Betrán AP, Torloni MR, Zhang J-J et al (2016). WHO statement on caesarean section rates. BJOG, 123(5):667-670.
23. Hamilton BE, Martin JA, Osterman M (2016). Births: Preliminary Data for 2015. Natl Vital Stat Rep, 65(3):1-15.
24. Bahadori F, Hakimi S, Heidarzade M (2013). The trend of caesarean delivery in the Islamic Republic of Iran/Évolution des accouchements par césarienne en République islamique d'Iran. East Mediterr Health J, S63-70.
25. Fabri R, Murta E (2015). Socioeconomic factors and cesarean section rates. Int J Gynaecol Obstet, 76(1):87-88.
26. Kenny LC, Lavender T, McNamee R et al (2013). Advanced maternal age and adverse pregnancy outcome: evidence from a large contemporary cohort. PloS one, 8(2):e56583.
27. Roos N, Sahlin L, Ekman-Ordeberg G, Kieler H, Stephansson O (2010). Maternal risk factors for postterm pregnancy and cesarean delivery following labor induction. Acta Obstet Gynecol Scand, 89(8):1003-1010.
28. Ronsmans C, Holtz S, Stanton C (2006). Socioeconomic differentials in caesarean rates in developing countries: a retrospective analysis. Lancet, 368(9546):1516-1523.
29. Elvander C, Högberg U, Ekeus C (2012). The influence of fetal head circumference on labor outcome: a population‐based register study. Acta Obstet Gynecol Scand, 91(4):470-475.
30. Wang S-C (2003). Artificial neural network. In: Interdisciplinary computing in Java programming. New York, NY, USA: Springer.
31. Park S, Choi C, Kim B, Kim J (2013). Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci, 68(5):1443-1464.
32. Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2):361-378.
33. Li J, Malley JD, Andrew AS, Karagas MR, Moore JH (2016). Detecting gene-gene interactions using a permutation-based random forest method. BioData Mining, 9, 14.
34. Ayer T, Chhatwal J, Alagoz O, Kahn Jr CE, Woods RW, Burnside ES (2010). Comparison of logistic regression and artificial neural network models in breast cancer risk estimation. Radiographics, 30(1):13-22.
35. Hsieh C-H, Lu R-H, Lee N-H et al (2011). Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. Surgery, 149(1):87-93.
2. Betrán AP, Ye J, Moller A-B, Zhang J et al (2016). The Increasing Trend in Caesarean Section Rates: Global, Regional and National Estimates: 1990-2014. PLoS One, 11(2):e0148343.
3. Cunningham F, Leveno K, Bloom S, Hauth J, Rouse D, Spong C (2005). Williams Obstetrics, 25th ed. New York, NY, USA: McGraw-Hill.
4. Allen VM, O'Connell CM, Liston RM, Baskett TF (2003). Maternal morbidity associated with cesarean delivery without labor compared with spontaneous onset of labor at term. Obstet Gynecol, 102(3):477-482.
5. Häger RM, Daltveit AK, Hofoss D et al (2004). Complications of cesarean deliveries: rates and risk factors. Am J Obstet Gynecol, 190(2):428-434.
6. Hebert PR, Reed G, Entman SS, Mitchel Jr EF et al (1999). Serious maternal morbidity after childbirth: prolonged hospital stays and readmissions. Obstet Gynecol, 94(6):942-947.
7. Murphy D, Stirrat G, Heron J, Team AS (2002). The relationship between Caesarean section and subfertility in a population-based sample of 14 541 pregnancies. Hum Reprod, 17(7):1914-1917.
8. Owen J, Andrews WW (1994). Wound complications after cesarean sections. Clin Obstet Gynecol, 37(4):842-855.
9. Schuitemaker N, Roosmalen J, Dekker G et al (1997). Maternal mortality after cesarean section in The Netherlands. Acta Obstet Gynecol Scand, 76(4):332-334.
10. Levine EM, Ghai V, Barton JJ, Strom CM (2001). Mode of delivery and risk of respiratory diseases in newborns. Obstet Gynecol, 97(3):439-442.
11. Towner D, Castro MA, Eby-Wilkens E, Gilbert WM (1999). Effect of mode of delivery in nulliparous women on neonatal intracranial injury. N Engl J Med, 341(23):1709-1714.
12. Miri Farahani L, Abbasi Shavazi MJ (2012). Caesarean section change trends in iran and some demographic factors associated with them in the past three decades. J Fasa Univ Med Sci, 2(3):127-134.
13. Movahed M, Enayat H, Ghaffarinasab E et al (2012). Related factors to choose cesarean rather than normal delivery among Shirazian pregnant women. J Fasa Univ Med Sci, 2(2):78-83.
14. Jiawei H, Kamber M, Pei J (2012). Data mining: concepts and techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann.
15. Kantardzic M (2011). Data mining: concepts, models, methods, and algorithms, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons.
16. Hernandez-Suarez G, Sanabria M, Serrano M, Zabaleta J, Tenesa A (2013). TGFBR1 and TP53 SNPs interactions associated with colorectal cancer risk: Analysis of metabolic pathways using a Random Forest approach. Cancer Res, 73(8):4840-4840.
17. Milewski R, Milewska AJ, Więsak T, Morgan A (2013). Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment. Studies in Logic, Grammar and Rhetoric, 35(1):39-48.
18. Amini P, Maroufizadeh S, Hamidi O, Samani RO, Sepidarkish M (2016). Factors associated with macrosomia among singleton live-birth: A comparison between logistic regression, random forest and artificial neural network methods. Epidemiol Biostat Public Health, 13(4):e119851-e119859.
19. Breiman L (2001). Random forests. Machine Learning, 45(1):5-32.
20. Dreiseitl S, Ohno-Machado L (2002). Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 35(5):352-359.
21. Ayat S, Farahani HA, Aghamohamadi M et al (2013). A comparison of artificial neural networks learning algorithms in predicting tendency for suicide. Neural Comput Appl, 23(5):1381-1386.
22. Betrán AP, Torloni MR, Zhang J-J et al (2016). WHO statement on caesarean section rates. BJOG, 123(5):667-670.
23. Hamilton BE, Martin JA, Osterman M (2016). Births: Preliminary Data for 2015. Natl Vital Stat Rep, 65(3):1-15.
24. Bahadori F, Hakimi S, Heidarzade M (2013). The trend of caesarean delivery in the Islamic Republic of Iran/Évolution des accouchements par césarienne en République islamique d'Iran. East Mediterr Health J, S63-70.
25. Fabri R, Murta E (2015). Socioeconomic factors and cesarean section rates. Int J Gynaecol Obstet, 76(1):87-88.
26. Kenny LC, Lavender T, McNamee R et al (2013). Advanced maternal age and adverse pregnancy outcome: evidence from a large contemporary cohort. PloS one, 8(2):e56583.
27. Roos N, Sahlin L, Ekman-Ordeberg G, Kieler H, Stephansson O (2010). Maternal risk factors for postterm pregnancy and cesarean delivery following labor induction. Acta Obstet Gynecol Scand, 89(8):1003-1010.
28. Ronsmans C, Holtz S, Stanton C (2006). Socioeconomic differentials in caesarean rates in developing countries: a retrospective analysis. Lancet, 368(9546):1516-1523.
29. Elvander C, Högberg U, Ekeus C (2012). The influence of fetal head circumference on labor outcome: a population‐based register study. Acta Obstet Gynecol Scand, 91(4):470-475.
30. Wang S-C (2003). Artificial neural network. In: Interdisciplinary computing in Java programming. New York, NY, USA: Springer.
31. Park S, Choi C, Kim B, Kim J (2013). Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci, 68(5):1443-1464.
32. Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2):361-378.
33. Li J, Malley JD, Andrew AS, Karagas MR, Moore JH (2016). Detecting gene-gene interactions using a permutation-based random forest method. BioData Mining, 9, 14.
34. Ayer T, Chhatwal J, Alagoz O, Kahn Jr CE, Woods RW, Burnside ES (2010). Comparison of logistic regression and artificial neural network models in breast cancer risk estimation. Radiographics, 30(1):13-22.
35. Hsieh C-H, Lu R-H, Lee N-H et al (2011). Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. Surgery, 149(1):87-93.
| Files | ||
| Issue | Vol 47 No 12 (2018) | |
| Section | Original Article(s) | |
| Keywords | ||
| Cesarean section Primiparas Artificial neural network Random forest Logistic regression Classification | ||
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How to Cite
1.
MAROUFIZADEH S, AMINI P, HOSSEINI M, ALMASI-HASHIANI A, MOHAMMADI M, NAVID B, OMANI-SAMANI R. Determinants of Cesarean Section among Primiparas: A Comparison of Classification Methods. Iran J Public Health. 2018;47(12):1913-1922.
