Download A retrospective evaluation of a data mining approach to fetal asphyxia at delivery in a hospital database

A retrospective evaluation of a data
mining approach to predict fetal
asphyxia at delivery in a hospital
database
Olavi Kauhanen, Seppo Heinonen
Department of Obstetrics and Gynecology,
Kuopio University Hospital, Finland
Winter in Kuopio, Finland
Background
• prenatal care is one of the
most frequently used health
services
• databases designed to monitor
health practices and guide
policy debates are needed
• key aspects of care leading to
optimal birth outcomes are
h f
Clinical significance of
asphyxia
• an umbilical artery base deficit
greater than 12-16 mmol/L
• the reasons to avoid perinatal
hypoxia is to prevent death of
the fetus and to reduce the risk
of permanent neurological damage
and complications in the
cardiovascular system,
respiratory system and kidneys,
insofar as these may be due to
oxygen deprivation at delivery
Clinical significance of low
birth weight
• newborn weight less than 2500 g may be
associated either to preterm delivery
or abnormal growth in utero in term
infants or both.
• the long-term developmental outcomes
of low birth weight infants are
characterised by impaired growth and
delay in speech development
• low birth weight results is
substantial costs to the health sector
following the infant´s discharge from
hospital and imposes a substantial
Aims of the study
• prediction of at-risk pregnancies is a dilemma for
obstetricians
• to identify pregnancies at risk is a prerequisite for
any selective strategy to prevent this complication
• to evaluate, using three data mining techniques
(logistic regression, decision tree induction and neural
network), maternal and obstetric risk factors of
intrapartum asphyxia and low birth weight
Study population
• We reviewed the total population who gave birth
between January 1990 and December 1998 at Kuopio
University Hospital
• Data were collected and analyzed retrospectively
from 556 women with fetal asphyxia at delivery and
from 21746 control women among whom umbilical
vein and artery blood gas and acid-base analysis had
been carried out.
Data mining analysis
• the patient sample (N=18273) was split into a training
(N=7309), validation (N=5482) and test set (N=5482) of
cases in the first test run
• in the second sample (N=18273) a random sample was
taken (N=1000) for scoring. The training set was 12270,
the validation 5259 patients
Data mining analysis
• We employed a default neural network of 66 input units.
The input units correspond to 66 reproductive risk factors
• For the purpose of performance assessment and method
comparison receiver operating characteristics (ROC)
curves were used. The area under the fitted ROC curves
(AUC) was calculated
ROC chart of models of the target (Asphyxia)
ROC chart of models of the target
(Low birth weight)
Diagram of the analysis (asphyxia)
Diagram of the analysis (low birth weight)
Diagnostic chart, N=3765
Diagnostic chart, N=5058
Asphyxia, ROC-chart, N=3765, training
data=3012, validation data=753, test=0
Asphyxia, ROC-chart, N=5058, training
data=3541, validation data=1517, test=0
Low birth weight, N=23939, training=19951,
validation=2494, test=2494
Low birth weight, N=8351,
training=6681, validation=1670, test=0
Low birth weight, scoring of the test data (N=1850), in SAS code node, N=24939
Frequency
Percent
Row Pct
Col Pct
0
1
Total
0
1021
55.19
58.48
97.52
725
1746
39.19 94.38
41.52
90.29
1
26
1.41
25.00
2.48
78
104
4.22 5.62
75.00
9.71
Total
1047 803
1850
56.59 43.41 100.00
Low birth weigth, scoring of the test data (N=1850), in SAS code node, N=8351
Frequency
Percent
Row Pct
Col Pct
0
1
Total
0
1416
76.54
81.10
97.25
330
1746
17.84 94.38
18.90
83.76
1
40
2.16
38.46
2.75
64
104
3.46 5.62
61.54
16.24
Total
1456 394
1850
78.70 21.30 100.00
Asphyxia, scoring of the test data (N=1851), in SAS code node, N=3765
Frequency
Percent
Row Pct
Col Pct
0
1
Total
0
1383
74.72
75.91
99.21
439
1822
23.72 98.43
24.09
96.06
1
11
0.59
37.93
0.79
18
29
0.97 1.57
62.07
3.94
Total
1394 457
1851
75.31 24.69 100.00
Asphyxia, scoring of the test data (N=1851), in SAS code node, N=5058
Frequency
Percent
Row Pct
Col Pct
0
1
Total
0
1466
79.20
80.46
99.26
356
1822
19.23 98.43
19.54
95.19
1
11
0.59
37.93
0.74
18
29
0.97 1.57
62.07
4.81
Total
1477 374
1851
79.79 20.21 100.00
Asphyxia, scoring of the test data (N=1851), in SAS code node (N=5058) by
using logistic regression
Frequency
Percent
Row Pct
Col Pct
0
1
Total
0
1474
79.63
80.90
99.26
348
1822
18.80 98.43
19.10
95.08
1
11
0.59
37.93
0.74
18
29
0.97 1.57
62.07
4.92
Total
1485
80.23
366
19.77
1851
100.00
Asphyxia, distribution of scores of the test data, training
data N=3012, initial analysis
Asphyxia, distribution of scores of the test data, training
data N=3541, further analysis
Low birth weight, distribution of scores of the test data,
training data N=19951, initial analysis
Low birth weigth, distribution of scores of test data,
training data N=6681, further analysis
Conclusions
- In the current study, we present a systematic,
practical approach to developing risk prediction
systems to be used with large databases of obstetric
information.
- We use logistic regression, tree and neural network
models to select variables to predict the risks of a
specific adverse pregnancy outcomes, such as asphyxia
at delivery and low birth weight.
Conclusions
- an important part of this approach is a novel feature
selection algorithm which uses the area under the receiver
operating characteristic (ROC) curve to measure the
expected discriminative power of different sets of
predictor variables
- collectively, we conclude that better prediction
performance requires more discriminative clinical
information rather than improved modeling techniques
and further studies with more numerous variables are
needed to improve the sensitivity for clinical purposes
Thank You
1
An retrospective evaluation of a data mining approach to
predict fetal asphyxia at delivery in a hospital database
Olavi Kauhanen
Seppo Heinonen
Department of Obstetrics and Gynecology, Kuopio University
Hospital, Finland
Running head: Risk factors of asphyxia and low birth
weight
2
ABSTRACT
Prenatal care is one of the most frequently used health
services. Therefore care takers and researchers have
become more interested in managing large administrative
databases designed to monitor health practices and guide
policy debates. This approach would aid in determining
which particular components of prenatal care are key
aspects of care leading to optimal birth outcomes.
Data mining tools providing simple and effective methods
of extracting knowledge from general medical information
were used to identify high risk patients, define the most
important factors (37 reproductive variables) leading to
fetal asphyxia at delivery and to low birth weight
infants, and thereafter build a multivariate relationship
model to show the relationship between any two variables
in a way that such relationships are easy to view. The
first part of this study tested the value of the data
mining (SAS Enterprise Miner) approach in predicting the
occurrence of fetal asphyxia (2%) and low birth weight
infants in an unselected, general obstetric population of
23,284 women. Three data mining models, logistic
regression, neural networks and decision tree induction,
were used in the analysis. In the second part of the
study, results were compared with each other in assessment
node of EM. Neural networks yielded with the first data
set quite a similar and with the second data set greater
percentage of correct classifications (AUC=.52 in the
first, AUC 0.66 in the second) with regard to the
outcomes. Collectively, in predicting low birth weight
infants neural network and tree methods appeared to be
more efficient than logistic regression. Our evaluation
showed that the data mining approach had a promising
predictive value in identifying early signals of fetal
asphyxia, and with a very large amount of information to
evaluate, such an approach is less open to human error
3
than the former statistical methods.
However, further studies with more numerous variables are
needed to improve the sensitivity for clinical purposes.
4
Introduction
Asphyxia
The reasons to avoid perinatal hypoxia is to
prevent death of the fetus and to reduce the risk of
permanent neurological damage and complications in the
cardiovascular system, respiratory system and kidneys,
insofar as these may be due to oxygen deprivation at
delivery.(1-4) The diagnosis of intrapartum fetal asphyxia
is feasible with blood gas and acid-base assessment of
umbilical cord blood at delivery, and clinically, the
commonly used threshold for significant metabolic acidosis
is an umbilical artery base deficit greater than 12-16
mmol/L.(5,6) However, the relationship between intrapartum
asphyxia and permanent neurological damage seems to be
poor, since 90% of children with cerebral palsy have been
found not to have suffered asphyxia at birth when the
significance of fetal acidemia in relation to an infant´s
long-term normality has been clarified in follow-up
studies. (7-10) Similarly, a single postnatal marker used
to identify high-risk infants, such as a low Apgar score
or the need for resuscitation in the delivery room, also
rarely predicts cerebral palsy. (11,12)
Although less neurological damage secondary to fetal
oxygen deprivation exists in the intrapartum interval than
had been thought previously, it is vital to identify fetal
hypoxia at delivery sufficiently early before neurological
damage has occurred.
Low birth weight
Low birth weight (less than 2500 g) may be associated
either to preterm delivery or abnormal growth in utero in
term infants or both. The causes of preterm delivery may
be broadly divided into three groups: induced preterm
deliveries, premature rupture of membranes and preterm
labor. In an identical manner, the etiologies of fetal
growth restriction can be divided into fetal, placental
5
and maternal factors. Collectively, the risk of low birth
weight is associated with a variety of sociodemographic
factors, multiple births, black parental race and the
adequacy of prenatal care.13-17 The long-term developmental
outcomes of low birth weight infants are characterised by
impaired growth and delay in speech development although
the prevalence of disability is related to a number of
biologic and environmental factors and the level of
perinatal-neonatal care. 18-20 Furthermore, low birth weight
results is substantial costs to the health sector
following the infant´s discharge from hospital and imposes
a substantial burden on special education and social
services. 21
Aims of the study
In obstetric care early recognition of events related to
intrapartum asphyxia on the basis of known reproductive
risk factors is difficult, and therefore, prediction of
at-risk pregnancies is a dilemma for obstetricians.
Research into the underlying mechanisms and etiological
factors of fetal asphyxia, to identify pregnancies at risk
is a prerequisite for any selective strategy to prevent
this complication. Basically, the same holds true, as
regards the risk of low birth weight. To carry out
accurate risk assessment, it is essential to identify
medical and social conditions that may put the woman or
the fetus at risk. The key question is: "who needs
prenatal care, and why?" Goodwin et al. used data mining
methods to produce results that were between 64-75%
accurate in predicting preterm birth in a racially diverse
sample of 19,970 women.22 This study was carried out to
evaluate, using three data mining techniques (logistic
regression, decision tree induction and neural network),
maternal and obstetric risk factors of intrapartum
asphyxia and low birth weight in a Finnish population
receiving modern obstetric care and having high
expectations about the safety of their unborn children.
6
MATERIALS AND METHODS
Selection of patients
We reviewed the total population who gave birth between
January 1990 and December 1998 at Kuopio University
Hospital which was the tertiary referral centre for
obstetric and perinatal care in East Finland, and served a
population of about 1 million people. Demographic and
outcome variables were collected prospectively at prenatal
visits and at delivery. Pregnant women had had routine
prenatal health care in maternity care units where
antenatal care was in the hands of general practitioners
and community midwifes. In Finland up to 95% of women book
for antenatal care by 14 weeks and are seen at intervals
free of charge. Minimum care for normal multigravidas
entails an average of six antenatal visits, and 8-10
visits for primiparous women. Pregnant women were referred
for antenatal care at our institution for clinical
reasons, usually when premature delivery was anticipated
or another specific pregnancy complication was discovered.
Primary surveillance for these subjects consisted of nonstress
tests
(23)
(NST)
and
amniotic
fluid
index
assessment
using
the
four
quadrant
amniotic
fluid
index.(24) The results were considered non-reassuring when
the NST was non-reactive or the amniotic fluid volume was
under 5 cm. Either a contraction stress test or a
biophysical profile was carried out as a backup test to
the abnormal primary screening result. Induction of labor
or cesarean delivery was performed if the backup tests
gave non-reassuring results.
Diagnostic criteria
Umbilical blood gas analysis was routine on every
delivery. Umbilical blood was collected into a
preheparinized syringe from a double-clamped section of
each cord for immediate acid-base analysis. The pH and
base deficit was obtained using a standard blood gas
analyzer, and extracellular base deficit was derived from
a nomogram.(25) Data were collected prospectively and
analyzed retrospectively from 556 women with fetal
7
asphyxia at delivery and from 21746 control women among
whom umbilical vein and artery blood gas and acid-base
analysis had been carried out. Only singleton,
structurally normal pregnancies going beyond 24 weeks were
considered for the analysis and 547 women were excluded
because information on pregnancy history, birth weight,
sex or estimated gestational age was missing or
implausible. Basic clinical data had been collected by the
team that took care of the delivery. The criterion of
intrapartum fetal asphyxia was an umbilical artery base
deficit > 12 mmol/L.
The documented clinical risk factors included those in the
obstetric history and maternal, obstetric, fetal, and
labor complications. To record the outcome of pregnancy
we used the following definitions: preterm birth delivery before 37 completed weeks of pregnancy; preeclampsia - repeated blood pressure measurements > 149/90
mmHg with proteinuria > 0.5 g/day; low birth weight newborn weight < 2500 g. Reference values for newborn
weight were obtained from the controls. The child was
considered small for gestational age when the sex- and
age-adjusted birth weight was below the normal 10th
percentile according to our own records. The data is also
presented with the cut-off level of the 5th centile for
comparison. If subjects had two abnormalities, such as LBW
and preterm delivery, each was considered an independent
risk factor and was included in both tallies.
Statistics
Differences between study subjects and controls were
tested for significance by X2 statistics (dichotomous
variables), and, where the minimal estimated expected
value was < 5, Fisher´s exact test was applied. A P value
of < 0.05 was considered statistically significant. A
two-tailed pooled t test was used to analyse continuous
variables. Possible confounding variables were identified
from background data, obstetric risk factors and health
behaviour. Multivariate analysis of significant or nearly
8
significant effects (P<0.1) was based on multiple
logistic regression analysis (BMDP Statistical Software
Inc., Los Angeles, CA). Statistical significance of
outcome measures was defined as P < 0.05, and 95 %
confidence intervals (CI) were determined.
Data mining analysis
Data that was recorded after birth, and thus not available
for pregnancy prediction models, was excluded from the
final analysis. The patient sample (N=18273) was split
into a training (N=7309), validation (N=5482) and test set
(N=5482) of cases in the first test run.
In the second sample (N=18273) a random sample was taken
(N=1000) for scoring. The training set was 12270, the
validation 5259 patients. There was no test set, because
we wanted as great as possible training set.
The selection of subjects for the inclusion of the two
sets was made in the partition node of EM. We employed a
default neural network of 35 input units in first sample
and 66 in the second sample. The input units correspond
to 35/66 reproductive risk factors (Table 1). In the
larger sample we have used 24 additional variables, that
were coded from the five nominal variables. Thus, the
variable classes were divided into new binary variables
(Table 1).
Positive values of the output represent a correct
diagnosis of either fetal asphyxia or low birth weight.
Each subject is therefore represented initially by a set
of 33/66 variables. The network provides a non-linear
transformation that converts this representation into an
estimate of the likelihood that subject is either affected
or unaffected. After an assessment node has been added to
the neural network, and then it was further connected to
logistic regression and tree nodes. The assessment node
was connected to score, distribution explorer and SAS code
nodes sequentially to evaluate SAS score code. Either all
or selected variables were included to the analysis. These
9
three models are known tolerate certain degree of
redundancy, and based our experience, it appeared useful
sometimes. Redundancy in this context implies, that the
same variable may be included in several different forms,
such as age as continuous or as binary variable. Results
are shown in Figure 3 (for asphyxia) and 4 (for LBW).
Additionally, for the purpose of performance assessment
and method comparison receiver operating characteristics
(ROC) curves were used. The area under the fitted ROC
curves (AUC) was calculated. Sensitivity and specificity
percents are derived from the confusion matrices of each
model.
The study was approved by the Research-Ethics Committee of
Kuopio University Hospital.
RESULTS
The incidence of intrapartum fetal asphyxia was 2.5 %. By
using logistic-regression, we simultaneously controlled
for all significant (P<0.10) reproductive risk factors,
and the adjusted risks for each factor are presented Table
2. Placental abruption, primiparity, alcohol use during
pregnancy, low birth weight, pre-eclampsia, male fetuses,
and small-for-gestational age births were independent risk
factors of intrapartum asphyxia, with adjusted relative
risks of 3.74, 3.10, 1.75, 1.57, 1.49, 1.48 and 1.33,
respectively. In an identical manner, after adjustments
also significant risk factors for LBW are presented in
Table 3.
Comparison between three methods is briefly presented in
Table 3. The ROC curves reflecting each methods
performance compared with each other are shown in Figure 1
and Figure 2. Furthermore, separate cross tabulation
tables are given to present the yielded sensitivity at a
given specificity for both asphyxia and LBW.
10
DISCUSSION
In the current study, we present a systematic, practical
approach to developing risk prediction systems to be used
with large databases of obstetric information. We use
logistic regression, tree and neural network models to
select variables to predict the risks of a specific
adverse pregnancy outcomes, such as asphyxia at delivery
and low birth weight. An important part of this approach
is a novel feature selection algorithm which uses the area
under the receiver operating characteristic (ROC) curve to
measure the expected discriminative power of different
sets of predictor variables. Collectively, we conclude
that better prediction performance requires more
discriminative clinical information rather than improved
modeling techniques.
Intrapartum asphyxia complicated one in 40 pregnancies
(2.5 percent); the frequency being the same in the
training and validation set. This study presents regional
outcomes, avoiding the selection bias inherent in multicenter studies. We found that intrapartum asphyxia is
associated with a number of factors, including placental
abruptions, primiparity, pre-eclampsia, alcohol use during
pregnancy, male fetuses, low birth weight and small for
gestational age births. The neural network performed
better (AUC .64) than the logistic regression model.
With regard to low birth weight it complicated one in 20
pregnancies (4.72 percent). Logistic regression of EM
revealed that placental abruption, advanced maternal age,
elevated liver entzymes, previous miscarriage, unmarried
status, placenta praevia, primiparity, preeclampsia,
maternal smoking and unemployment are significant risk
factors. Neural network yielded a quite similar result
(AUC 0.59) to logistic regression (AUC=0.60), wheras tree
model was found to be the most appropriate method (AUC
11
0.64) for this data set.
Typically, prediction of asphyxia with an incidence of
approximately 2% in the general obstetric population
exemplifies "detection of rare classes" also highlighted
in EM on-line manual. Principally, the same holds true
for low birth weight. Because many risk factors also occur
with low incidences resolution of analysis requires high
degree performance. EM has high resolution, but another
requirement is that the sample size is large enough to
draw conclusions. With a small number of cases the model
suffers from lability. This was clearly noted also in the
current investigation. Firstly, the results were difficult
to reproduce in successive analyses, and secondly, minor
changes in input variables resulted in significant
differences in output variables. Therefore, further
variables and a larger sample size are needed to improve
the power. The other side of the coin is that we should be
able to predict adverse outcomes, whether they are rare or
not, in the future. Of course fetal asphyxia should not be
the only outcome measure considered but it must be one of
the most important one.
References:
1. Goldaber KG, Gilstrap LC, Levene KJ, Dax JS, McIntyre
JS. Pathologic fetal acidemia. Obstet Gynecol
1991;78:1103-6.
2. Goodwin TM, Belai I, Hernandez P, Durand M, Paul RH.
Asphyxial complications in the term newborn with severe
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umbilical acidemia. Am J Obstet Gynecol 1992;162:150612.
Low JA, Galbraith RS, Muir DW, Killen HL, Pater EA,
Karchmar EJ. Motor and cognitive deficits after
intrapartum fetal asphyxia in the mature fetus. Am J
Obstet Gynecol 1988;158:356-61.
Low JA, Galbraith RS, Muir DW, Killen HL, Pater EA,
Karchmar EJ. Mortality and morbidity after intrapartum
asphyxia in the preterm fetus. Obstet Gynecol
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Winkler CL, Hauth JC, Tucker MJ, Owen J, Brumfield CG.
Neonatal complications at term as related to the degree
of umbilical cord acidemia. Am J Obstet Gynecol
1991;164:637-41.
Low JA, Panagiotopoulos C, Derrick EJ. Newborn
complications after intrapartum asphyxia with metabolic
acidosis in the term fetus. Am J Obstet Gynecol
1994;170:1081-7.
Blair E, Stanley FJ. Intrapartum asphyxia: a rare cause
of cerebral palsy. Journal of Paediatrics 1988;112:5159.
8. Richardson BS, Rurak D, Patrick JE, Homan J, Carmichael
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9. Gunn AJ, Parer JT, Mallard EC, Williams CE, Gluckman PD.
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injury and subsequent cerebral palsy: medicolegal
issues. Pediatrics 1997;99:851-9.
13. Rodriguez C, Regidor E, Gutierrez-Fisac JL. Low birth
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weight in Spain associated with sociodemographic
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14.Goldenberg RL, Cliver SP, Mulvihill FX, Hickey CA,
Hoffman HJ, Klerman LV, Johnson MJ. Medical,
psychosocial, and behavioral risk factors do not explain
the increasedrisk for low birth weight among black
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15.Hessol NA, Fuentes-Afflick E, Bacchetti P. Risk of low
birth weight infants among black and white parents.
Obstet Gynecol 1998;92:814-22.
16.Elster N. Less is more: the risks of multiple births.
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Group on Reproductive Technology. Fertil Steril 2000
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17.Haas JS, Orav EJ, Goldman L. The relationship between
physicians' qualifications and experience and
theadequacy of prenatal care and low birthweight. Am J
Public Health 1995;85:1087-91.
18.Stutchfield P, Nicklin S, Minchom P, Powell T, Kelly A,
Klimach V, Davies R,Horrocks S. Assessment of health
status at two years of very low birthweightinfants-clinical governance. Clin Perform Qual Health Care
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in the extremely low-birth weight infant. Clin Perinatol
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Crockett P, Dreiseitl S, Vinterbo S, Hammond W. Building
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15
__________________________________________________________
Table 1. List of variables
__________________________________________________________
Age (as continuous variable)
Age < 18
Age > 35
Unemployed
Yes
No
Missing
Not married
Educational background
Base grade
Middle grade
High grade
Missing
Work hours
Domestic work
Regular day shifts
Rotating shift schedule: mornings and evenings
Shift work
Night shifts only
Missing or implausible
Physical requirements
Domestic work
Light sedentary work
Strenuous sedentary work
Work requires standing and walking
Strenuous manual work
Missing
Socioeconomic status
Employer
Entrepreneur
White collar worker
Blue collar worker
Pensioner
Student
Other
Missing or implausible
Maternal length
Maternal weight
Pregravid body mass index
Pregravid BMI > 25
16
Smoking (>5 cigarettes/day)
Alcohol consumption
Primiparity
Miscarriage
Prior termination
¾ 7 deliveries
Prior cesarean
Second pregnancy in 12 months
Interval since previous delivery >6 y
Prior fetal demise
Chronic disease
Infertility
Maternal diabetes
Placental abruption
Preeclampsia
Prematurity
Small-for-gestational age (10th percentile)
Low birth weight
Placenta previa
Velamentous umbilical cord insertion
Elevated liver enzymes
Prolonged gravid
Late pregnancy bleeding
Gender
Isoimmunization (Rh)
Low hemoglobin concentration (<100 g/L)
17
TABLE 2
Logistic Regression model for predictor variables
Risk factor for
asphyxia
OR
95 % CI
Placental
abruption
Primiparity
Alcohol
consumption
Low birth weight
Pre-eclampsia
Male fetus
Small-forgestational age
Elevated liver
enzymes
Post-dates
3.74
2.15 - 6.51
3.10
1.75
2.57 - 3.74
1.18 - 2.59
1.57
1.49
1.48
1.33
1.18
1.06
1.24
1.04
1.66
0.799 - 3.46
1.34
0.942 - 1.91
Infertility
1.26
0.942 - 1.68
Amniotic
infection
Second pregnancy
in 12 months
Unmarried
1.15
0.273 - 4.81
1.12
0.721 - 1.75
1.01
0.833 - 1.22
Prematurity
0.926
0.633 - 1.36
Previous
miscarriage
0.835
0.637 -1.09
OR = odds ratio
CI = confidence interval
-
2.09
2.08
1.77
1.70
18
TABLE 3
Logistic Regression model to predict low birth weight
infant (List of significant factors)
Risk factor for a
Low birth weight
OR
Placental
abruption
Primiparity
Alcohol
consumption
Pre-eclampsia
Elevated liver
enzymes
Infertility
8.99
Second pregnancy
in 12 months
Unmarried
Previous
miscarriage
1.86
1.49
17.2
2.85
1.42
1.39
1.32
1.13
OR = odds ratio
CI = confidence interval
19
TABLE 4
Results from EM
DATA/
Vars
PARTITION
TARGET
METHOD
SETTINGS
etc
AUC
SENSITIVI
TY
SPECIFICI
TY
N=18273
Vars=34
Tr=7309
Va=5482
Te=5482
ASFYKSIA
Log reg
Default
0.57
21
93
Neural
0.52
14
91
Tree
81 hidden
neurons
Default
0.65
28
87
Log reg
Default
0.60
29
84
Neural
Tree
Default
Default
0.64
0.57
50
32
78
89
Log reg
Default
0.66
51
86
Neural
Tree
Default
Default
0.68
0.64
48
38
88
95
N=17259
Vars=66
N=17259
Vars=66
Tr=12270
Va=5259
Tr=12270
Va=5259
ASFYKSIA
LBW
20
FIGURE 1 (ASFYXIA, from EM)
21
FIGURE 2 (LBW, from EM)
22
FIGURE 3. Cross tables for observed asphyxia by prediction
coded from P_ASFYES, values > 0.05
TABLE of ASFYKSIA by predicted ASFYXIA
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚
0‚
1‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0 ‚
756 ‚
218 ‚
974
‚ 75.60 ‚ 21.80 ‚ 97.40
‚ 77.62 ‚ 22.38 ‚
‚ 98.18 ‚ 94.78 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1 ‚
14 ‚
12 ‚
26
‚
1.40 ‚
1.20 ‚
2.60
‚ 53.85 ‚ 46.15 ‚
‚
1.82 ‚
5.22 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
770
230
1000
77.00
23.00
100.00
coded from P_ASFYES, values > 0.04
TABLE of ASFYXIA by predicted ASFYXIA
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚
0‚
1‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0 ‚
669 ‚
305 ‚
974
‚ 66.90 ‚ 30.50 ‚ 97.40
‚ 68.69 ‚ 31.31 ‚
‚ 98.38 ‚ 95.31 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1 ‚
11 ‚
15 ‚
26
‚
1.10 ‚
1.50 ‚
2.60
‚ 42.31 ‚ 57.69 ‚
‚
1.62 ‚
4.69 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
680
320
1000
68.00
32.00
100.00
23
FIGURE 4. Cross tables for low birth weight by predicted
low birth weight
TABLE of LBW by predicted LBW
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚
0‚
1‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0 ‚
710 ‚
246 ‚
956
‚ 71.00 ‚ 24.60 ‚ 95.60
‚ 74.27 ‚ 25.73 ‚
‚ 97.13 ‚ 91.45 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1 ‚
21 ‚
23 ‚
44
‚
2.10 ‚
2.30 ‚
4.40
‚ 47.73 ‚ 52.27 ‚
‚
2.87 ‚
8.55 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
731
269
1000
73.10
26.90
100.00
24
*** END OF PAPER ***
Correspondence:
Olavi Kauhanen
Department of Obstetrics and
Gynecology
Kuopio Unversity Hospital
70211 Kuopio
Phone: 358-17-172360
FAX: 358-17-172378
E-mail: [email protected]
Because we had very short time to do this paper, we
reserve facility send checked one later.