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Nom original: genetic-algorithm.pdfTitre: Estimating time to full uterine cervical dilation using genetic algorithmAuteur: Jeong-Kyu Hoh; Kyung-Joon Cha; Moon-Il Park; Mei-Ling Ting Lee; Young-Sun Park

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Kaohsiung Journal of Medical Sciences (2012) 28, 423e428

Available online at www.sciencedirect.com

journal homepage: http://www.kjms-online.com

ORIGINAL ARTICLE

Estimating time to full uterine cervical dilation using
genetic algorithm
Jeong-Kyu Hoh a, Kyung-Joon Cha b, Moon-Il Park a, Mei-Ling Ting Lee c,
Young-Sun Park b,*
a

Department of Obstetrics and Gynecology, College of Medicine, Hanyang University Hospital, Seoul,
South Korea
b
Department of Mathematics, Research Institute of Natural Sciences, Hanyang University, Seoul, South Korea
c
Department of Epidemiology and Biostatistics, Biostatistics and Risk Assessment Center, University
of Maryland, College Park, MD, USA
Received 30 May 2011; accepted 4 October 2011
Available online 9 April 2012

KEYWORDS
Cervical dilation;
Genetic algorithm;
Labor curves;
Nulliparous

Abstract The objectives of this study were to provide new parameters to better understand
labor curves, and to provide a model to predict the time to full cervical dilation (CD). We
studied labor curves using the retrospective records of 594 nulliparas, including at term, spontaneous labor onset, and singleton vertex deliveries of normal birth weight infants. We redefined the parameters of Friedman’s labor curve, and applied a three-parameter model to the
labor curve with a logistic model using the genetic algorithm and the NewtoneRaphson method
to predict the time necessary to reach full CD. The genetic algorithm is more effective than
the NewtoneRaphson method for modeling labor progress, as demonstrated by its higher accuracy in predicting the time to reach full CD. In addition, we predicted the time (11.4 hours) to
reach full CD using the logistic labor curve using the mean parameters (the power of
CD Z 0.97 cm/hours, a midpoint of the active phase Z 7.60 hours, and the initial
CD Z 2.11 cm). Our new parameters and model can predict the time to reach full CD, which
can aid in the forecasting of prolonged labor and the timing of interventions, with the end goal
being normal vaginal birth.
Copyright ª 2012, Elsevier Taiwan LLC. All rights reserved.

* Corresponding author. Department of Mathematics, Research Institute of Natural Sciences, Hanyang University, 17 Haengdang-dong,
Sungdong-gu, Seoul 133-791, South Korea.
E-mail address: pppppys@hanyang.ac.kr (Y.-S. Park).
1607-551X/$36 Copyright ª 2012, Elsevier Taiwan LLC. All rights reserved.
doi:10.1016/j.kjms.2012.02.012

424

Introduction
Labor is the presence of uterine contractions of sufficient
frequency, duration, and intensity to cause demonstrable
effacement and dilation of the cervix [1]. It is a continuum
that culminates in birth. Multiple fixed factors such as
parity, maternal weight, and fetal weight, as well as
commonly employed interventions (e.g., oxytocin
augmentation and epidural use) may significantly affect the
duration of labor [2]. Childbirth is a complex and dynamic
process that incorporates cervical dilation (CD), uterine
contractions, and the descent of the fetal head. CD and the
duration of CD are good diagnostic indicators of prolonged
labor [3e6]. Friedman [7] produced a chart to illustrate the
relationship between CD and the duration of the labor
process and to aid in differentiation between normal and
abnormal labors. He also established a series of definitions
of labor protraction and arrest. These definitions have been
widely adopted and applied in practice in the past halfcentury [8]. However, as there is no universal definition
of “normal” labor, and diagnosing prolonged labor is
inherently difficult [9].
Some studies have argued that the Friedman curve is no
longer appropriate for induced or actively managed labor
[10,11]. As a reassessment of the labor curve, Zhang et al.
[12] used repeated-measures regression with a 10th-order
polynomial function to define an average labor curve,
suggesting that the pattern of labor progression in
contemporary practice differs significantly from the Friedman curve [12]. Vahratian et al. [13] provided an overview
of Friedman’s work, addressed methodological challenges
in studying labor progression, and described the utility of
more advanced statistical methods for studying labor
progression, such as survival analysis, compared with other
approaches. Some of these studies have focused mainly on
assisting in the diagnosis of prolonged labor and the timing
of interventions, with the aim of achieving natural vaginal
birth, but they have not identified useful tools for predicting prolonged labor.
We therefore reconsider full CD in this study, redefine
the parameters of Friedman’s labor curve (e.g., latent and
active phase, deceleration phase), and develop clinically
useful statistical models. We applied a three-parameter
logistic model to the labor curve and redefined the three
parameters of Friedman’s labor curve with a logistic model
using the genetic algorithm (GA) and NewtoneRaphson (NR)
method to predict the time necessary to reach full CD
[14,15]. We also evaluated and compared the performances
of the mathematical inference method (NR) and evolutionary computation method (GA).
To our knowledge, this is the first report of estimating
the time to full uterine CD using the GA.

J.-K. Hoh et al.
February 2009. Inclusion criteria were gestational age
between 38 and 42 weeks, spontaneous onset of labor,
vertex presentation at admission, CD <7 cm at admission,
and duration of labor from admission to delivery >3 hours
[16]. We performed CD measurements at least once per
hour, thereby establishing a consistent data collection. We
excluded cases involving cesarean delivery, labor induction, and/or epidural anesthesia.
This study was conducted with the approval of the
Institutional Review Board Committee of the Hanyang
University Hospital, Seoul, South Korea.

Redefining the parameters of Friedman’s labor
curve
The three-parameter logistic model is defined as follows:


yj Za= 1 þ exp b xj g
where a (>0) is the final size achieved, b (>0) is the scale
parameter, and g is the inflection point on the curve at the
vertical coordinate xj. When xj / N, for all and any yj,
each yj converges to 0 or a. At (xj, yj) Z (g, a/2), this curve
has a maximum slope of ab/4.
We applied an adjusted three-parameter logistic model
with an upper bound set to 10 cm and defined the lower
asymptote as the “initial CD” due to the different dilation
starting times. Dilation at each time (tj, the jth hour after
admission) was defined as yj (cm). The logistic model has an
upper asymptote, yj Z 10 (cm), defined as follows:


ð1Þ
yj Zc þ ð10 cÞ= 1 þ exp a tj b
Additionally, the three parameters (a, b, and c) are
described as follows:
a Z a scale parameter, is related to the maximum slope
(cm/h) at the point of inflection on the labor curve. This
parameter indicates the “power” of the CD, and it can be
used to determine the time it takes to reach full CD, which
is the final objective of our study.
b Z the location parameter at tj Z b (hours). Eq. (1) has
a maximum slope of a (10ec)/4 (cm/h) at the point of
inflection where (tj, yj) Z (b, (10 þ c)/2). This parameter is
“a midpoint of the active phase” in Friedman’s labor curve.
c Z is a lower asymptote, and is the initial CD, giving
a lower asymptote yj Z c (cm) as tj / eN. The upper
asymptote is yj Z 10 (cm) as tj / þ N. It is also used in
estimating CD at admission or before admission.
Fig. 1 shows a logistic labor curve in which a Z 0.92,
b Z 6.83 hours, c Z 0.75 cm, and the maximum slope is
2.13 cm/h.
The following is an example of what the equation, using
the above parameters, would look like:


yj Z0:75 þ ð10 0:75Þ= 1 þ exp 0:9 tj 6:83
ð2Þ

Materials and methods
Research participants

Estimation of a three-parameter logistic model
in the labor curve

We extracted the clinical record data of 594 singleton
nulliparous women in the last months of their pregnancies
who underwent vaginal delivery at Hanyang University
Hospital, Seoul, South Korea, between October 2004 and

GA is the most fundamental and widely known evolutionary
computation currently used in application research. The
most important feature in GA design is chromosome
encoding, as the chromosomes must be mapped to the sets

Estimating time to full cervical dilation

425

Figure 1. Definitions of the three parameters of the logistic
model in the labor curve. Note. The point of inflection
(hours, cm) Z (b, (10 þ c)/2); the maximum slope Z a (10 e
c)/4 Z 0.92 (10 e 0.75)/4 Z 2.13 (cm/hour), a(Z0.92), is
a power of cervical dilation.

of parameters that need to be estimated. We have adopted
a real value representation, rather than a classic binary
representation, for the application of the logistic model
Eq. (1).
When applying the GA (Appendix 1), the number of
chromosomes m was set to 100, and the number of
maximum iterations (i.e., gen) was set to 20,000. In the
selection process, after randomly choosing a number r from
(0, 1), the numerical value [100 r] ([ ] indicates a Gauss
function) was obtained by roulette wheel selection
(number of precision Z 6 and precision integer Z 2). The
arithmetical crossover operator’s weight was set to 0.8, the
crossover rate pc to 0.2, and the mutation rate pm to 0.01
(Appendix 2, Fig. I).
When applying the NR method (Appendix 3), we applied
PROC NLIN of the SAS package (Ver. 9.1; SAS Institute Inc.,
Cary, NC, USA) to the three-parameter logistic models, with
the initial values for each model based on the published SAS
methods described by Rogers et al. [17].

Results
Table 1 presents the baseline characteristics of the overall
study sample. Physicians performed an average of 10.05
Table 1 General characteristics of the study sample
(N Z 594).
Parameters
No. of pelvic examinations
Maternal age (y)
Gestational age at delivery (wk)
Cervical dilation at admission (cm)
Duration of first stage of labor (h)
Birth height (cm)
Birth weight (gm)
1-min Apgar score
5-min Apgar score

Mean SD
10.05
30.32
40.26
2.11
12.01
49.87
3396
6.95
8.88

Data are presented as mean standard deviation (SD).











2.37
2.46
0.11
1.68
5.19
2.16
310
0.25
0.33

pelvic examinations from admission to the first stage of
labor (standard deviation, 2.37). The mean age at the time
of delivery was 30.32 (2.46) years. The CD at the time of
admission was 2.11 (1.68) cm. The mean number of gestational weeks at delivery was 40.26 (0.11), and the first
stage of labor was 12.01 hours (5.19). The average weight
of the newborns was 3396 g (310). The average Apgar scores
were 6.95 (0.25) and 8.88 (0.33) at 1 and 5 minutes,
respectively.
We found that the prediction accuracy (sum of square
error; root mean square error; statistic measuring the
accuracy of forecast, U1; Theil’s inequality coefficient, U )
[18] of GA was higher than that of the NR method, and that
GA was more appropriate than NR in the optimization of the
three-parameter logistic model in the labor curve (Table 2).
As shown in Table 3, the time predicted by GA was closer
to the observed time than that predicted by NR. Hence, the
models estimated by GA can be regarded as more effective
for modeling labor progress than those estimated by NR.
The estimated labor curves at various dilations are given
in Figs. 2A and 2B. In Fig. 2A (top), the first labor curve (a.1)
used only four observed data-points (3 hours after admission), while (a.2) used five observed data-points (4 hours
after admission). In (a.4), the estimated arrival time to CD
(about 10 cm) was about 10.8 hours (root mean square
error Z 0.1), which was approximately the actual arrival
time of the patient (11.0 hours). As shown in Fig. 2B
(bottom), the initial CD of the labor curve (b) was smaller
than that of labor curve (a), and the “active phase” of labor
curve (b) began 7 hours after admission.
Fig. 3 shows various patterns of estimated labor curves
to full dilation by GA. The time to reach full CD in curve (b)
was longer than in curve (a) (5.4 vs. 13.6 hours), although
the CD on admission in curve (b) was bigger than curve (a)
(1 vs. 5 cm).

Discussion
In general, measuring CD can be subjective, and such
measurements are only estimates because observations are
rounded to the nearest centimeter [19]. This measure,
although generally accepted, may not be precise and there
are no reported trials of either interobserver or intraobserver reproducibility. Women are admitted into labor
and delivery at various levels of CD, and it is difficult to
predict the chances of a normal vaginal delivery and the
duration of labor in the first stage [2,20]. A nonparametric
method might also provide a direct fit to the data, but the
disadvantage of such methods is the inefficient use of
individual data on labor progression, in the sense that
estimates assume a cluster of points at neighboring dilations [21].
Friedman’s labor curve was derived from observations of
CD and fetal station plotted against time elapsed from the
onset of labor (in hours). The typical S-shaped curve for
most laboring women defines the normal limits for labors
with healthy outcomes or for identifying abnormal labor.
However, the management of labor and delivery has
changed since Friedman’s series of publications on evaluating labor in clinical practice. Specifically, there has been
an increased use of obstetric interventions during labor and

426
Table 2
method.

J.-K. Hoh et al.
Comparisons of estimated parameters and prediction accuracies for the genetic algorithm and the NewtoneRaphson

Parameters

Genetic algorithm
Mean SE

Scale parameter a
Location parameter b (h)
Lower asymptote c (cm)
Maximal slope (cm/h)
SSE
RMSE
U1
U

0.97
7.60
2.11
1.83
2.01
0.39
0.05
0.03










0.08
0.11
0.03
0.16
0.04
0.10
0.01
0.00

NewtoneRaphson

Median (range)
1.00
7.50
2.50
1.54
1.84
0.37
0.05
0.02

(0.33e9.97)
(0.99e16.80)
(0.00e6.57)
(0.70e21.12)
(0.00e4.91)
(0.00e5.52)
(0.00e1.10)
(0.00e0.41)

Mean SE
0.88
7.22
2.62
1.25
13.74
0.67
0.09
0.05










0.16
3.02
0.05
0.27
2.02
0.14
0.01
0.00

Median (range)
1.04
7.73
2.75
1.78
3.95
0.47
0.06
0.03

(0.33e32.54)
(0.93e775.90)
(0.00e6.92)
(0.62e65.09)
(0.00e287.00)
(0.00e5.55)
(0.00e1.10)
(0.00e0.43)

RMSE Z root mean square error; SE Z standard error; SSE Z sum of square error; U1 Z statistic measuring the accuracy of the forecast;
U Z Theil’s inequality coefficient.

delivery [22,23]. The debate over whether the deceleration
phase described by Friedman actually exists remains
unsettled [24].
Hoskins and Gomez [25] determined that abnormalities
in the Friedman curve were not useful predictors for
operative delivery in pregnancies complicated by fetal
macrosomia. Zhang et al. [12] found that the labor curve of
1329 ethnically diverse, nulliparous women differed markedly from the Friedman curve. Using repeated-measures
regression analysis, the duration of labor for fetal descent
at various stations, rate of CD at each phase of labor, were
described and compared with the Friedman curve. It was
noted that the cervix dilated substantially more slowly in
the active phase of the first stage of labor, taking approximately 5.5 hours to dilate from 4 to 10 cm compared with
2.5 hours in the Friedman model. Zhang et al. did not find
evidence of a deceleration (transition) phase in this study.
While labor curves may provide graphical depictions of
labor progression that are easier for physicians to visualize
in clinical practice, they are difficult to construct and
interpret accurately. They also have limited clinical utility,
especially for determining whether labor deviates from the
normal progression (e.g., duration of normal first stage
labor) [26].
In contrast, our model provides detailed real-time
information regarding the duration of CD, requiring only
three observed data-points (i.e., known onset of labor);
this has not been achieved in previous studies. Based on this
information, the model calculates latent and active phase

duration, maximum slope, deceleration phase duration,
and overall first stage duration.
The calibration (inverse estimation) of the latent (onset
of labor to 4 cm dilation) and active (4e10 cm) phases [27]
can be easily performed by using Eq. (3), which is induced
by Eq. (1) in the section “Redefining the parameters of
Friedman’s labor curve”:



tj Zð 1=aÞ loge 10 yj
ð3Þ
yj c þ b
For example, in Fig. 1 we can calculate the latent (6.16
hours; tj Z (e1/0.92) loge[(10 e 4.0)/(4.0e0.75)] þ 6.83)
and active phases (4.81 hours; Dtj Z 10.97 e 6.16) of the
first stage of labor. We also predict the time (11.4 hours) to
reach full CD using the logistic labor curve with mean
parameters (i.e., a Z 0.97, b Z 7.60, and c Z 2.11; Table 2).
Comparing the estimates of duration of labor across
studies, especially for the first stage, is difficult for several
reasons (e.g., different starting points to calculate the
duration of labor, variation in sample restriction, exclusion
of induced labor and cesarean deliveries) [28].
We are convinced that such biases could be reduced by
introducing mathematical modeling and a method known
for accuracy (the logistic and GA models, respectively). In
this research, we applied the three-parameter logistic
model to labor curves. Obviously, three measurements
obtained within a couple of hours of admission will not
yield a satisfactory “prediction accuracy.” Thus, a fourth
and a fifth addition of observed data-points to the above

Table 3 Comparison of predicted time to full cervical dilation (10 cm)a estimated by the genetic algorithm and the
NewtoneRaphson methods.
Parameters

Genetic algorithm
b

Time prediction (h)
MAE
MSE

NewtoneRaphson

Mean SE

Median (range)

Mean SE

Median (range)

11.44 0.11
0.50 0.02
0.45 0.02

11.75 (5.0e19.0)
0.31 (0.00e2.00)
0.05 (0.00e4.00)

12.17 0.34
0.92 0.15
3.02 1.48

12.00 (3.0e23.00)
0.88 (0.00e5.00)
0.32 (0.00e25.00)

MAE Z mean absolute error; MSE Z mean squared error; SE Z standard error.
a
Full cervical dilation (CD) is approximately 10 cm if the estimate of CD 9.8 cm.
b
95% confidence interval; genetic algorithm Z (11.22e11.68) versus NewtoneRaphson Z (12.43e12.91).

Estimating time to full cervical dilation

427

Figure 2. Estimated labor curves calculated using the genetic algorithm for (A) (a.1) 3 hours, (a.2) 4 hours, (a.3) 5 hours, and
(a.4) 6 hours of observation; (B) (b.1) 3 hours, (b.2) 4 hours, (b.3) 5 hours, and (b.4) 8 hours of observation after admission. Note.
(a.1) the first estimated labor curve used only four observed data-points (n Z 4, B; RMSE Z 2.4), and the 10 cm* arrival time was
approximately 7.8 hours (true, 11.0 hours), (a.4) n Z 7, 10.8 hours (RMSE Z 0.1); (b.1) n Z 4, 19.5 hours (RMSE Z 0.1; true, 19.0
hours), (b.4) n Z 9, about 18.7 hours (RMSE Z 0.1). * Full CD is approximately 10 cm if the estimate of CD 9.8 cm. CD Z cervical
dilation; RMS Z root mean square error.

steps will produce a more accurate result. The number of
parameters used in our study, 3 (a, b, and c), is chosen as
a minimum number of nonlinear model to explain the labor
curve. Using our three parameters not only enables users to
calculate the parameters currently used but also to
calculate the time to full uterine CD, and even the overall
duration of first stage using the time calculated. As for

predictive accuracy, we found that the parameters from
GA were closer to the optimal solution, and that GA was
better than the NR method at solving optimization problems. GA is more effective than NR for modeling labor
progress, as demonstrated by its higher accuracy in predicting the time to reach full CD. Our study focused on
estimating the time to full uterine CD using the GA, which

Figure 3. Estimated labor curves to full CD (approximately 10 cm of cervical dilation)* according to the genetic algorithm. (a) 5.4
hours, (b) 13.6 hours, (c) mean Z 11.4 hours (i.e., labor curve with a Z 0.97, b Z 7.60, c Z 2.11 in Table 3), (d) 20.0 hours, and (e)
17.8 hours. *Full cervical dilation (CD) is approximately 10 cm if the estimate of CD 9.8 cm.

428
is prioritized to any other, in order to figure out the “labor
mechanism.” In the future, we will be able to provide
direct “criteria for normal labor” subjected to greater data
further proving the model to be a useful one in prediction
of prolonged labor.
Our research had the following limitations. First, we
were unable to confirm that delays in the progression of
labor were actually associated with increases in incidence
of cesarean delivery, augmentation of labor, or number of
vaginal examinations [29]. Second, we were unable to
demonstrate whether there were any similarities between
the duration of labor and the pattern of labor progress
among nulliparous and multiparous mothers [30]. Finally,
our sample was drawn from a single tertiary care hospital in
South Korea, which may limit the generalizability of our
results. Regarding the problem of having study participants
from a hospital, we plan to conduct a more extensive
research through a multicenter study in the future.
In conclusion, we have developed a three-parameter
logistic model to predict the time to reach full dilation. This
model may be useful for diagnosis of prolonged labor and
the timing of interventions, and may facilitate the
achievement of normal vaginal birth.

Acknowledgments
This work was supported by the Cluster Research Fund of
Hanyang University (HY-2009) and Basic Science Research
Program through the NRF of Korea funded by the Ministry of
Education, Science and Technology (No. 2010-0005140).

Supplementary data
Supplementary data related to this article can be found
online at doi:10.1016/j.kjms.2012.02.012.

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