Iranian Journal of Medical Sciences

Document Type : Original Article(s)


1 Student Research Committee, School of Nursing and Midwifery, Shahroud University of Medical Sciences, Shahroud, Iran

2 Department of Reproductive Health and Midwifery, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran

3 Center for Health Related Social and Behavioral Sciences Research, Shahroud University of Medical Sciences, Shahroud, Iran

4 School of Allied Medical Sciences, Shahroud University of Medical Sciences, Shahroud, Iran

5 Department of Basic Sciences, School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran

6 Ophthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iran


Background: With the growing rate of cesarean sections, rising morbidity and mortality thereafter is an important health issue. Predictive models can identify individuals with a higher probability of cesarean section, and help them make better decisions. This study aimed to investigate the biopsychosocial factors associated with the method of childbirth and designed a predictive model using the decision tree C4.5 algorithm.
Methods: In this cohort study, the sample included 170 pregnant women in the third trimester of pregnancy referring to Shahroud Health Care Centers (Semnan, Iran), from 2018 to 2019. Blood samples were taken from mothers to measure the estrogen hormone at baseline. Birth information was recorded at the follow-up time per 30-42 days postpartum. Chi square, independent samples t test, and Mann-Whitney were used for comparisons between the two groups. Modeling was performed with the help of MATLAB software and C4.5 decision tree algorithm using input variables and target variable (childbirth method). The data were divided into training and testing datasets using the 70-30% method. In both stages, sensitivity, specificity, and accuracy were evaluated by the decision tree algorithm.
Results: Previous method of childbirth, maternal body mass index at childbirth, maternal age, and estrogen were the most significant factors predicting the childbirth method. The decision tree model’s sensitivity, specificity, and accuracy were 85.48%, 94.34%, and 89.57% in the training stage, and 82.35%, 83.87%, and 83.33% in the testing stage, respectively.
Conclusion: The decision tree model was designed with high accuracy successfully predicted the method of childbirth. By recognizing the contributing factors, policymakers can take preventive action.
It should be noted that this article was published in preprint form on the website of research square (


What’s Known

Previous studies showed that age, height, neonatal weight, high blood pressure, sugar, thyroid, toxemia, breech presentation, sleep disturbance, and multiple pregnancies are predictive factors for the childbirth method Further studies indicated 50 factors related to the childbirth method in four groups, pre-pregnancy, during the pregnancy, medical and social factors to predict the childbirth method.

What’s New

The previous childbirth method, maternal body mass index at childbirth, maternal age, and serum estrogen were the most significant factors predicting the childbirth method, and to the best of our knowledge, this study is the first to evaluate the estrogen hormone as a predictor of childbirth method The decision tree model was designed with high accuracy and sensitivity for predicting the childbirth method and by identifying the contributing factors, and the rules derived from the model, health practitioners and policymakers can take preventive measures.


It is estimated that the world’s population will double over 60 years (two billion by 2050). 1 In many countries, including Iran, due to an aging population, population control policies have become the incentive for childbearing. 2 , 3 One reason for this decrease in the fertility rate is the consequences of cesarean delivery, which has increased worldwide, and its rate is much higher than the World Health Organization (WHO) standards in Iran. 4 , 5 Cesarean section (CS) will intensify the process of decrease in fertility rates by prolonging the interval among subsequent pregnancies and increasing the probability of secondary infertility. 5 Moreover, placenta praevia, placenta accrete, and uterine rupture can be significantly increased in subsequent pregnancies after a primary CS. 6

Childbirth is a multidimensional process and a crucial experience in a mother’s life, which is a unique experience while being universal. 7 , 8 In certain instances, the childbirth method is determined based on mental health problems, ignorance, misconceptions, and attitudes, not based on medical indications. 9 Fear of labor pain is a most common and serious problem among women. 10 , 11 Psychological factors such as fear and stress can intensify labor pain, but it can be moderated by social support and self-efficacy. Moreover, higher maternal socioeconomic status and supplemental health insurance have shown to improve the CS rate. 12 Biological factors such as known hormones that contribute to labor progression (e.g., oxytocin and cortisol) are also linked to the psychological phenomena that may involve labor abnormality. 8 , 12 Labor abnormality and increased CS are also associated with the estrogen hormone. Pregnancy is a hyperestrogenic state, and the placenta is a major source of estrogen secretion. 13 Therefore, complex biological, psychological, and social factors influence the childbirth method, which can be clarified through a biopsychosocial model. 8

With the classification method, as a type of data mining, we can design a predictive model by identifying the different biopsychosocial variables. Classification is the process of finding a model that can identify unknown categories of other objects in order to identify the categories of data or data concepts. One of the common methods of classification is the decision tree. The decision tree is created by if-then rules for classification. Several algorithms are used in the decision tree construction, including ID3, C4.5, CHAID, and CART, the most important of which is the C4.5 algorithm. 14

Considering the increasing rate of CS in Iran, the effect of the childbirth method on different aspects of health, and the limited studies in Iran to predict the childbirth method, and since knowing the causes of CS can be a step toward reducing its rate, we decided to use the biopsychosocial model to investigate the associated factors and, by identifying these factors, design a model to predict the childbirth method using the C4.5 algorithm.

Materials and Methods

This cohort study was conducted on 170 pregnant mothers, who were referred to Shahroud Health Care Centers (Semnan, Iran) for pregnancy care during the third trimester of pregnancy from 2018 to 2019. The protocol of this study was reviewed and approved by the Institutional Review Board of Shahroud University of Medical Sciences (ethical code IR.SHMU.REC.1397.84). Explanations regarding study objectives and other necessary explanations were given to all participants, and written informed consents were taken for the publication of their clinical details. The inclusion criteria were as follows: Iranian citizenship, singleton pregnancy, having electronic records in the health system, no history of illness, and no onset of labor pain. A questionnaire, including demographic and socioeconomic variables, pregnancy history, and the WHO-5 well-being index, was completed.


WHO-5 well-being index: The WHO-5 questionnaire consists of five questions about the participant’s feelings during the previous two weeks, with each item being scored based on a six-point Likert scale of 0-5, which was initially developed to evaluate the quality of care for diabetic patients. 15 The validity and reliability of the Persian version of this scale were assessed in the Iranian population. 16 The validity of the WHO-5 questionnaire was assessed in a study on Iranian pregnant mothers up to eight weeks postpartum. Cronbach’s alpha for WHO-5 items was 0.85, and a score of 50 or less with a good sensitivity of 84% and a specificity of 59% was used to identify the psychological symptoms. 17 A 2015 review identified WHO-5 as a valid tool for depression screening. 18 In our study, this scale was evaluated to have substantial reliability, that was, 0.81.

Socioeconomic index: The socioeconomic status was constructed using principal component analysis (PCA) according to the method described by Vyas and colleagues, which combines three main factors, including economic indicators (occupation, spouse occupation, homeownership status, a separate bedroom for couples, number of bedrooms, indoor bathroom, cooking area), asset-index (refrigerator, freezer, color TV, washing machine, dishwasher, microwave, vacuum cleaner, personal car, landline, mobile phone, computer or laptop, internet access), and social factors (education, spouse education, family members, family supplementary health insurance). 19 , 20 At last, 15 variables were used to construct the socioeconomic status variables, which explained 17% of the total variance in this new variable. After calculating a variable called socioeconomic status, due to the twenty-fifth and seventy-fifth percentiles of these variables, the population was divided into high, medium, and low socioeconomic categories.

The height and weight of the mothers and their attendance in childbirth preparation classes were recorded. Then, 3 mL blood samples were taken from the antecubital vein in a non-fasting condition (from 9 a.m. to 11 a.m.) to measure the estrogen hormone. The blood samples were immediately transferred to the laboratory and centrifuged by the laboratory officer. Before analysis and after plasma seperation, the samples were frozen at -80 °C. After ensuring that childbirth data were collected, serum estradiol E2 levels were measured by the enzyme-linked immunosorbent assay (ELISA) (Monobind kit, China).

Childbirth information was recorded at the time of referral to the health centers at 30-42 days postpartum. Participants’ blood samples were examined at the end of the second stage.

It should be noted that through the cooperation, mothers’ questions about the research or problems related to pregnancy and so on were answered by phone. Counseling was provided in case of any problems and referrals were given, if further counseling or treatment was needed.

Input Variables: Maternal age, family socioeconomic status, the previous method of childbirth, maternal mental health status during pregnancy, maternal body mass index (BMI) at childbirth, and maternal serum estradiol E2.

Output Variables: Method of childbirth, including VD and CS.

Model Design: At this point, the data were divided into training and testing datasets using the 70-30% method, meaning that 70% of the data were deemed as the training data, and 30% were considered as the testing dataset. The decision tree algorithm was implemented and formed based on the training data. The decision was then evaluated based on training and testing datasets. Finally, regarding the foregoing and considering the known risk factors of the CS, such as old age, obesity, history of CS, and failure to perform vaginal delivery after cesarean (VBAC), 21 the rules were reviewed by the authors of the article, and the ones that were clinically valid and scientifically available were presented as the final rules.

Statistical Analysis

The data were analyzed using SPSS-23 (IBM, U.S.) and MATLAB 2017 (Mathworks, U.S.) software. Chi-square, Independent-Samples t test, and Mann-Whitney were used for the initial comparisons between the two groups. A P value<0.05 was considered as the significance level. Using input variables and determining the target variable, a C4.5 decision tree algorithm was developed.


Nulliparous mothers accounted for 43.6% of the sample; 41.1% were primiparous and 15.4% multiparous. Only 27% of the mothers had attended childbirth preparation classes. The childbirth method of half of them (48.5%) was CS (35.6% non-emergency and 12.8% emergency). The causes of emergency CS were as follows: intrauterine growth restriction (IUGR), borderline amniotic fluid index (AFI), tachycardia, uterine malformations, and breech presentation (4.8% each), cephalopelvic disproportion (CPD) (9.5%), non-response to induction (19%), and meconium-stained amniotic fluid (MSAF) (47.6%). There were 34 cases of repeated non-emergency second CS. In the case of four subjects due to emigration from Shahroud and 10 subjects due to non-cooperation, the required information was obtained by telephone. Finally, 163 subjects were analyzed (figure 1). As seen in table 1, the mean±SD of variables such as age, previous method of childbirth, and maternal mental health status in pregnancy were significantly different between the two groups. Still, the socioeconomic status, serum estradiol level, gestational age at sampling time, and maternal BMI at childbirth were not significantly different.

Figure 1. The flow chart illustrates the process of recruitment and follow-up of a pregnancy cohort.

Variable Total N=163 VD N=84 CS N=79 P value
Age (yr.) (Mean±SD) 29.08±5.76 27.9±5.89 30.33±5.38 0.007 a
BMI at Birth (kg/m2) (Mean±SD) 30.26±4.08 29.81±4.2 30.73±3.92 0.15 a
Estradiol E2 (pg/ml) (Mean±SD) 3407.02±1388.99 3481.01±1352.86 3328.35±1430.83 0.48a
Gestational age (week) (Mean±SD) 31.81±3.32 31.72±3.32 31.9±3.34 0.7b
Socioeconomic status index (%) 0.6c
Low 41 (25.2) 21 (25.0) 20 (25.3)
Medium 82 (50.3) 45 (53.6) 37 (46.8)
High 40 (24.5) 18 (21.4) 22 (27.8)
Previous childbirth method (%) 0.001c
None 71 (43.6) 43 (51.2) 28 (35.4)
VD 45 (27.6) 41 (48.8) 4 (5.1)
CS 45 (27.6) 0 45 (57.0)
VD & CS 2 (1.2) 0 2 (2.5)
Maternal mental health status (%) 0.04c
≥50 26 (16.0) 9 (10.7) 17 (21.5)
>50 137 (84.0) 75 (89.3) 62 (78.5)
aIndependent-Samples t test; bMann-Whitney; cChi-square; VD: Vaginal delivery; CS: Cesarean section; BMI: Body mass index
Table 1.The comparison of demographic and socio-economic variables between two groups of childbirth methods

Model Construction

Training phase: In this phase, 70% of the 163 subjects were used in the training model. With the help of training data, the decision tree was created (table 2). However, the accuracy of these measures was to be tested in the testing phase.

Training Datasets Testing Datasets
Confusion Matrix Sensitivity Specificity Accuracy PPV NPV Confusion Matrix Sensitivity Specificity Accuracy PPV NPV
53 9 85.48% 94.34% 89.57% 94.64% 84.75% 14 3 82.35% 83.87% 83.33% 73.68% 89.66%
3 50 5 26
Table 2.The output of tree algorithm for training and test datasets

In a decision tree, the most important information is the set of rules, consisting of several logical implications (if-then rule). Figure 2 shows the tree structure and presents the set of rules created by the C5 model.

Evaluation phase: A total of 48 samples were used for testing. The model output in the testing phase was compared with the actual results (table 2). The data were used to predict the decision tree rules. Some of the most important rules that were approved are as follows:

  1. 1. Mother’s previous method of childbirth can predict the next method of childbirth with 95.65% probability.
  2. 2. A nulliparous mother with a BMI of less than 24.55 gives a VD with a 66.67% probability.
  3. 3. A nulliparous mother with ≥20.5 years of age, a BMI≥24.55 at the time of childbirth, and estradiol E2≥4702 pg/ml will have a VD with a probability of 88.89%.
  4. 4. A nulliparous mother with ≥20.5 years of age, a BMI≥24.55 at the time of childbirth, and estradiol E2 levels below 1097.1 pg/ml will have a CS with 66.67% probability.

Figure 2. Flowchart demonstrates the proposed tree structure. VD: Vaginal delivery; CS: Cesarean section; BMI: Body mass index


Given the specific variables of a pregnant mother, CS probability is estimated based on the obtained rules. The previous method of childbirth, maternal BMI at childbirth, maternal age, and serum estrogen were the most significant variables predicting the type of childbirth, and the decision tree model predicted the method of childbirth with 89.57% accuracy in the training phase and 83.33% accuracy in the testing phase. In our study, there were no cases of VBAC; in other words, 100% of the CSs were performed without trying labor first. This was in line with results from many other studies, which concluded that CS in the first pregnancy leads to CS in later pregnancies, with a high probability. 12 , 22 - 25 Moreover, in line with our study, findings also suggested that obesity is a contributing factor. 26 Prior studies indicated that obesity led to a two to three-fold increase in the risk of emergency CS in primiparous and multiparous women. 27 Furthermore, according to the present study and other researches, maternal age, especially in the first birth, is an important factor affecting CS. 12 , 28 , 29 Estrogen hormone is another factor associated with labor abnormality and the CS thereafter. Estrogens induce myometrial changes, including increased production of prostaglandin (PG) E2 and F2α by the expression of the PG receptor enhancer, increased expression of the oxytocin receptor, an adrenergic agonist modulating membrane calcium channels, increased connexin synthesis, gap junction formation in the myometrium, and up-regulation of enzymes responsible for muscle contraction, such as light chain kinase myosin and calmodulin. All these changes result in synchronous uterine contractions that may facilitate VD. 13

In our study, maternal mental health in the third trimester of pregnancy and socioeconomic status were not predictive factors for the childbirth method. However, studies suggest that mental health problems before and during pregnancy, higher socioeconomic status, and possession of private health insurance are associated with higher CS rates. 30 - 33

Based on a previous study, the decision tree model was able to predict the childbirth method by an accuracy of 100% and 99.5 % in training and testing datasets. In this study, age, height, neonatal weight, high blood pressure, sugar, thyroid, toxemia, breech presentation, sleep disturbance, and multiple pregnancies were the predictive factors of the childbirth method. 12 Another study indicated 50 factors related to the childbirth method in four groups, pre-pregnancy, during the pregnancy, medical, and social factors to predict the childbirth method. In that study, the factors most predictive of CS were medical factors. The decision tree model and artificial intelligence classified 80% and 82% of the childbirth results, respectively. 34 Moreover, other models were also used to predict the method of childbirth. Factors affecting maternal VD in another study were the age of mother (20-25 years old), multiparity, unscarred uterus, rhesus positivity, expected baby weight of 2.5-3.5 kg, cephalic presentation, Bishop status>4, spontaneous onset of labor, clear liquor, and no fetal heart rate (FHR) abnormality. The composite score of ≤21 predicted VD with 80% sensitivity and 65% specificity. Still, a score above 21 did not always indicate CS. 35

This was a cohort study, and we had a sample loss of 8%. To solve this problem, we had to call the mothers on the phone to acquire information about the childbirth method, which reduced the information’s accuracy. On the other hand, due to the low rate of delivery in emergency CS with labor, emergency CS without labor, and elective CS groups, we had to place everyone in one group (CS).


Data mining techniques can be used in the health care system by developing appropriate models to predict the likelihood of disease occurrence. Data collected from pregnant mothers, including estrogen hormone, were analyzed, and a high-precision model was designed using the C5 algorithm. To the best of our knowledge, this study is the first one to evaluate the estrogen hormone as a predictor of the childbirth method. In order to reduce the rate of CS in screening programs, it is recommended to implement this model with a larger sample size, after reaching the desired accuracy level. Health practitioners and policymakers can take preventive measures by identifying the contributing factors and the rules derived from this model.


Hereby, the authors of this article present their thanks and appreciation to Shahroud University of Medical Sciences. The present study was supported by Shahroud University of medical sciences as a PhD Thesis (grant No. 9740).

Conflict of Interest: None declared.


  1. Kanasi E, Ayilavarapu S, Jones J. The aging population: demographics and the biology of aging. Periodontol 2000. 2016; 72:13-8. DOI | PubMed
  2. Karamouzian M, Sharifi H, Haghdoost AA. Iran’s shift in family planning policies: concerns and challenges. Int J Health Policy Manag. 2014; 3:231-3. Publisher Full Text | DOI | PubMed
  3. Liu J, Liu M, Zhang S, Ma Q, Wang Q. Intent to have a second child among Chinese women of childbearing age following China’s new universal two-child policy: a cross-sectional study. BMJ Sex Reprod Health. 2019; 46:59-66. Publisher Full Text | DOI | PubMed
  4. Bahl R, Strachan B, Murphy DJ. Outcome of subsequent pregnancy three years after previous operative delivery in the second stage of labour: cohort study. BMJ. 2004; 328:311. Publisher Full Text | DOI | PubMed
  5. Setudezadeh F, Yousefinezhadi T. The increasing prevalence of cesarean in Iran: how the rate of cesareans could be controlled. Obstet Gynecol Int J. 2019; 9:532-5. DOI
  6. Zhao J, Shan N, Yang X, Li Q, Xia Y, Zhang H, et al. Effect of second child intent on delivery mode after Chinese two child policy implementation: a cross sectional and prospective observational study of nulliparous women in Chongqing. BMJ Open. 2017; 7:e018823. Publisher Full Text | DOI | PubMed
  7. Salehi A, Fahami F, Beigi M. The effect of presence of trained husbands beside their wives during childbirth on women’s anxiety. Iran J Nurs Midwifery Res. 2016; 21:611-5. Publisher Full Text | DOI | PubMed
  8. Saxbe DE. Birth of a new perspective? A call for biopsychosocial research on childbirth. Current Directions in Psychological Science. 2017; 26:81-6. DOI
  9. Vafaee R, Hosseini F, Ghobadi Dashdebi K, Momen Bellah Fard MJ, Ghalandari M, Gharlipour Z, et al. Assessing the factors influencing delivery method selection in pregnant women referred to Public hospitals in Shiraz. SBMU Journals. 2013; 23:13-8.
  10. Ebrahimzadeh Zagami S, Golmakani N, Saadatjoo SA, Ghomian N, Baghbani B. The shape of uterine contractions and labor progress in the spontaneous active labor. Iran J Med Sci. 2015; 40:98-103. Publisher Full Text | PubMed
  11. Andaroon N, Kordi M, Ghasemi M, Mazlom R. The Validity and Reliability of the Wijma Delivery Expectancy/Experience Questionnaire (Version A) in Primiparous Women in Mashhad, Iran. Iran J Med Sci. 2020; 45:110-7. Publisher Full Text | DOI | PubMed
  12. Kavitha D, Balasubramanian T. Prediction the mode of delivery and the risk factors association with cesarean delivery using decision tree model. International Journal of Engineering Sciences and Research Technology. 2018; 7:116-24.
  13. Kota SK, Gayatri K, Jammula S, Kota SK, Krishna SV, Meher LK, et al. Endocrinology of parturition. Indian J Endocrinol Metab. 2013; 17:50-9. Publisher Full Text | DOI | PubMed
  14. Utgoff PE. Incremental induction of decision trees. Machine learning. 1989; 4:161-86. DOI
  15. Bradley C, Lewis KS. Measures of psychological well-being and treatment satisfaction developed from the responses of people with tablet-treated diabetes. Diabet Med. 1990; 7:445-51. DOI | PubMed
  16. Khosravi A, Mousavi SA, Chaman R, Kish MS, Ashrafi E, Khalili M, et al. Reliability and validity of the Persian version of the World Health Organization-five well-being index. International Journal of Health Studies. 2015; 1:17-9. DOI
  17. Mortazavi F, Mousavi SA, Chaman R, Khosravi A. Validation of the World Health Organization-5 Well-Being Index; assessment of maternal well-being and its associated factors. Turk Psikiyatri Derg. 2015; 26:48-55. PubMed
  18. Topp CW, Ostergaard SD, Sondergaard S, Bech P. The WHO-5 Well-Being Index: a systematic review of the literature. Psychother Psychosom. 2015; 84:167-76. DOI | PubMed
  19. Damghanian M, Shariati M, Mirzaiinajmabadi K, Yunesian M, Emamian MH. Socioeconomic inequality and its determinants regarding infant mortality in iran. Iran Red Crescent Med J. 2014; 16:e17602. Publisher Full Text | DOI | PubMed
  20. Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006; 21:459-68. DOI | PubMed
  21. Firoozi M, Tara F, Ahanchian MR, Latifnejad Roudsari R. Health Care System Barriers to Vaginal Birth after Cesarean Section: A Qualitative Study. Iran J Nurs Midwifery Res. 2020; 25:202-11. Publisher Full Text | DOI | PubMed
  22. Brennan DJ, Robson MS, Murphy M, O’Herlihy C. Comparative analysis of international cesarean delivery rates using 10-group classification identifies significant variation in spontaneous labor. Am J Obstet Gynecol. 2009; 201:308 e1-8. DOI | PubMed
  23. Dahlen HG, Homer CS. ‘Motherbirth or childbirth’? A prospective analysis of vaginal birth after caesarean blogs. Midwifery. 2013; 29:167-73. DOI | PubMed
  24. Homer CS, Johnston R, Foureur MJ. Birth after caesarean section: changes over a nine-year period in one Australian state. Midwifery. 2011; 27:165-9. DOI | PubMed
  25. Karlstrom A, Nystedt A, Johansson M, Hildingsson I. Behind the myth--few women prefer caesarean section in the absence of medical or obstetrical factors. Midwifery. 2011; 27:620-7. DOI | PubMed
  26. Al-Kubaisy W, Al-Rubaey M, Al-Naggar RA, Karim B, Mohd Noor NA. Maternal obesity and its relation with the cesarean section: a hospital based cross sectional study in Iraq. BMC Pregnancy Childbirth. 2014; 14:235. Publisher Full Text | DOI | PubMed
  27. Lynch CM, Sexton DJ, Hession M, Morrison JJ. Obesity and mode of delivery in primigravid and multigravid women. Am J Perinatol. 2008; 25:163-7. DOI | PubMed
  28. Baghurst P, Robson S, Antoniou G, Scheil W, Bryce R. The association between increasing maternal age at first birth and decreased rates of spontaneous vaginal birth in South Australia from 1991 to 2009. Aust N Z J Obstet Gynaecol. 2014; 54:237-43. DOI | PubMed
  29. Smith GC, Cordeaux Y, White IR, Pasupathy D, Missfelder-Lobos H, Pell JP, et al. The effect of delaying childbirth on primary cesarean section rates. PLoS Med. 2008; 5:e144. Publisher Full Text | DOI | PubMed
  30. Fairley L, Dundas R, Leyland AH. The influence of both individual and area based socioeconomic status on temporal trends in Caesarean sections in Scotland 1980-2000. BMC Public Health. 2011; 11:330. Publisher Full Text | DOI | PubMed
  31. Roberts CL, Algert CS, Carnegie M, Peat B. Operative delivery during labour: trends and predictive factors. Paediatr Perinat Epidemiol. 2002; 16:115-23. DOI | PubMed
  32. Wangel AM, Molin J, Moghaddassi M, Stman M. Prior psychiatric inpatient care and risk of cesarean sections: a registry study. J Psychosom Obstet Gynaecol. 2011; 32:189-97. DOI | PubMed
  33. Wangel AM, Molin J, Ostman M, Jernstrom H. Emergency cesarean sections can be predicted by markers for stress, worry and sleep disturbances in first-time mothers. Acta Obstet Gynecol Scand. 2011; 90:238-44. DOI | PubMed
  34. Sana A, Razzaq S, Ferzund J. Automated diagnosis and cause analysis of cesarean section using machine learning techniques. International Journal of Machine Learning and Computing. 2012; 2:677. DOI
  35. Kotha S, Kushtagi P. Prediction of mode of delivery in term pregnancies: development of scoring system. International Journal of Reproduction, Contraception, Obstetrics and Gynecology. 2015; 4:1283-90. DOI