28

Japanese Journal of Comprehensive Rehabilitation Science (2020)

Original Article

Relationship between the number of samples and the accuracy ofthe prediction model for dressing independence using artificial
neural networks in stroke patients
Takaaki Fujita, OTR, PhD,1 Takuro Ohashi, OTR,2 Kazuhiro Yamane, OTR,2
Yuichi Yamamoto, RPT,2 Toshimasa Sone, OTR, PhD,1 Yoko Ohira, MD, PhD,2
Koji Otsuki, MD, PhD,2 Kazuaki Iokawa, OTR, PhD3
Department of Rehabilitation, Faculty of Health Sciences, Tohoku Fukushi University, Sendai, Miyagi, Japan
Department of Rehabilitation, Kita-Fukushima Medical Center, Date, Fukushima, Japan3
Preparing Section for New Faculty of Medical Science, Fukushima Medical University, Fukushima, Fukushima,
Japan
1
2

ABSTRACT
Fujita T, Ohashi T, Yamane K, Yamamoto Y, Sone T,
Ohira Y, Otsuki K, Iokawa K. Relationship between
the number of samples and the accuracy of theprediction model for dressing independence using
artificial neural networks in stroke patients. Jpn J
Compr Rehabil Sci 2020; 11: 28‐34.
Objective: To determine the lower limit of the numberof samples that is useful for creating a prediction
model on dressing independence in stroke patients byusing artificial neural networks.
Methods: Five datasets consisting of 120, 100, 80, 60,
and 40 were created from 121 stroke patients by
repeated random sampling. The models for predicting
independent dressing one month after admission werecreated by an artificial neural network and logistic
regression in each dataset from the variables uponadmission to the convalescent rehabilitation ward. The
accuracy of both models was compared.
Results: The accuracy of the artificial neural networkmodel was significantly higher than that of the logistic
regression model in the 120, 100, and 80 patient
datasets, and there were no differences in the accuracyof both models in the 60 and 40 patient datasets.
Conclusion: Our results suggested that the lower limitof the number of samples for creating a usefulprediction model of dressing independence by using
artificial neural networks is approximately 80.
Correspondence: Takaaki Fujita, OTR, PhD
Department of Rehabilitation, Faculty of Health Sciences,
Tohoku Fukushi University, 6‐149‐1 Kunimigaoka,
Aoba-ku, Sendai, Miyagi 989‐3201, Japan.
E-mail: t-fujita@tfu-mail.tfu.ac.jp
Accepted: January 7, 2020.
Conflicts of interest: There are no conflicts of interest to
declare.
©Kaifukuki Rehabilitation Ward Association 2020
doi.org/10.11336/jjcrs.11.28

Key words: stroke, prediction, activities of dailylivingIntroduction
Stroke is one of the leading causes of long-term
disability [1]. In rehabilitation, the prognosis of theactivities of daily living (ADLs) should be predicted to
set goals, plan interventions, prepare the necessary
human and physical environments, and promote family
support. Various reports have investigated different
ADL prognostic methods; however, the generalizationof results is often limited [2, 3]. The creation of an ADL
prediction model in each facility has been shown to
improve the accuracy of the prediction [4].
However, the number of usable data is small whencreating a prediction model at each single facility.
Fujita et al. [5] examined a method that creates ahighly accurate ADL prediction model even for small
samples at each single facility and reported that
artificial neural networks are useful. They also reported
that when a prediction model for dressing independence
was created using 83 samples, the accuracy of theartificial neural network model was higher than that oflogistic regression and decision trees. However, thelower limit of the number of samples needed tomaintain sufficient accuracy of the artificial neuralnetwork has not been clarified. Thus, we examined therelationship between the accuracy of the prediction
model based on artificial neural networks and thenumber of learning samples to determine the lower
limit of the number of samples needed to create a
useful prediction model for dressing independence
using artificial neural networks.

Jpn J Compr Rehabil Sci Vol 11, 2020

Fujita T et al.: Relationship between the number of samples and accuracy of an artificial neural network model
29

Methods
This was a retrospective observational study thatcollected and analyzed information from the medical
records of patients. A total of 121 stroke patients who
were admitted and discharged from the convalescentrehabilitation ward of a hospital were recruited into
this study. The inclusion criteria were follows: (1)diagnosis of initial cerebral hemorrhage or cerebral
infarction, (2) unilateral supratentorial lesion, (3)inability to dress independently upon admission (≤5
points for the FIM® instrument [version 3] [6, 7],
dressing the upper body, lower body, or both), and (4)no missing analysis data described later. This study
was reviewed and approved by the ethical review
boards of Kita-Fukushima Medical Center and Tohoku
Fukushi University (no. 72, RS180601).
Models that predict whether patients can
independently dress themselves at one month after
admission were studied. Dressing independence wasassessed on the basis of the score in the FIM®
instrument in dressing the upper body and lower bodyitems at one month after admission. The FIM®
instrument dressing items consist of the upper andlower bodies; however, the lower scores of both wereadopted in this study: ≥6 points indicates independenceand ≤5 points indicates dependence. The following
were collected as dependent variables: age [8],dressing performance prior to practice [9], trunk
function [8, 10], visuospatial perception [8], andbalance [11, 12] upon admission, which have beenreportedly associated with dressing performance. The
FIM® instrument score for dressing item was used as
an index of dressing performance upon admission, theStroke Impairment Assessment Set (SIAS) [13] wasused as trunk function and visuospatial recognitionindices, and the Berg balance scale (BBS) [14] as abalance index. In addition to these variables, the upper
limb function on the affected and unaffected sides,
cognitive function, and poststroke duration, whichhave been reportedly associated with ADL prognosis,
were also added in this study. The Simple Test for
Evaluating Hand Function (STEF) [15] was used as anindex of upper limb function, whereas the revised
Hasegawa Dementia Scale (HDS-R) [16] was used as
an index of cognitive function.
Five datasets of 120, 100, 80, 60, and 40 were createdfrom 121 stroke patients by repeated random sampling toinvestigate the relationship between the accuracy andnumber of samples of the prediction model for dressing
independence created by artificial neural networks.
Thereafter, the accuracy of the prediction model createdusing each dataset was compared. The accuracy of themodel created using the artificial neural network was
examined by comparing it with the model created bylogistic regression, which is a widely used technique. The
procedure used for creating a model by using artificial
neural networks and logistic regression was as follows:

first, patients were classified into two categories, namely,
the dressing independent group (FIM® instrument
dressing item of ≥6 points at one month after admission)and the dressing dependent group (FIM® instrument
dressing item of ≤5 points). Each variable upon admission
was compared between the groups to select the variables
to be included in the prediction model in each of the fivedata sets with different numbers of samples. Student’s
t-test, the chi-square test, and the Mann-Whitney U test
were used for comparison. Second, the logistic regression
with stepwise forward selection method (likelihood ratio)
was performed using the variable upon admission, which
was significant by comparison between groups, as anindependent variable and the dressing independence ordependence at one month postadmission as a dependent
variable. The artificial neural network model was ahierarchical multilayer perceptron with one intermediate
layer in this study, whereas the independent variable wasselected using the logistic regression to create the logistic
regression and artificial neural network models with thesame independent variable. To prevent overfitting, theratio between learning and testing samples (used to trackerrors during training) in the artificial neural network was
set to 9:1. Furthermore, when variables consisting of ≥5grade scale (i.e., items except SIAS trunk and visuospatial
perception) were used as independent variables in theartificial neural network, they were included in the model
after being converted into a four-grade scale based on thequartile by considering the number of small samples. Inmodels created with multilayer perceptron, the initialvalues for weighting from the input to the intermediate
layer are randomly determined, and the prediction
accuracy is dependent on these values. Thus, the initialvalues were reset 10 times, and the model with the highest
prediction accuracy was used.
The accuracy of models created using the artificial
neural network and logistic regression was comparedby stratified 10-fold cross-validation. The dataset was
randomly split 10-fold, and the model was createdusing 9 parts of the dataset. The model accuracy wasverified using the remaining data, and this process was
repeated 10 times. In this procedure, logistic regression
with the forced entry method was used to retain the
independent variables used in the model. In the 10fold cross-validation, the classification accuracy,
sensitivity, specificity, positive predictive value, andnegative predictive value in the artificial neuralnetwork and logistic regression models were calculated
and compared using the Wilcoxon rank sum test. The
level of significance was set at 5% for all tests, and all
analyses were performed using SPSS Statistics version
25 (IBM Corp., Armonk, NY, USA).
Results
Table 1 shows the attributes, cognitive and physicalfunctions, and dressing performance of the patients ineach dataset. As regards the results of the comparison
Jpn J Compr Rehabil Sci Vol 11, 2020

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Fujita T et al.: Relationship between the number of samples and accuracy of an artificial neural network model

Table 1. Attributes, physical and cognitive functions, and dressing performance of the study patients in eachdataset.
Variables
Age, years, mean (SD)
Gender, men, n (%)
Affected side, right, n (%)
Post-stroke time at admission, days,
mean (SD)
SIAS verticality at admission, points,
median (IQR)
SIAS abdominal muscle strength atadmission, points, median (IQR)
SIAS visuospatial perception atadmission, points, median (IQR)
Berg balance scale at admission,
points, mean (SD)STEF affected side at admission,
points, mean (SD)STEF unaffected side at admission,
points, mean (SD)HDS-R at admission, points, mean
(SD)
Dressing performance at admission
FIM® upper body dressing, points,
median (IQR)
FIM® lower body dressing, points,
median (IQR)
The lower score on FIM® for dressing
the upper and lower body, points,
median (IQR)
Dressing performance at 1-monthafter admission
FIM® upper body dressing, points,
median (IQR)
FIM® lower body dressing, points,
median (IQR)
The lower score on FIM® for dressing
the upper and lower body, points,
median (IQR)
Independence of upper body dressing,n (%)
Independence of lower body dressing,n (%)
Independence of both upper andlower body dressing, n (%)

Dataset 1(n = 120)

Dataset 2(n = 100)

Dataset 3(n = 80)

Dataset 4(n = 60)

Dataset 5(n = 40)

75.2 (12.4)
62 (51.7)
56 (46.7)

75.4 (12.6)
57 (57.0)
51 (51.0)

75.6 (13.0)
43 (53.8)
37 (46.3)

74.8 (13.0)
32 (53.3)
25 (41.7)

74.8 (14.3)
22 (55.0)
20 (50.0)

30.2 (11.7)

29.3 (11.2)

29.4 (11.1)

31.4 (12.0)

28.4 (11.9)

3 (2‐3)3 (2‐3)3 (2‐3)3 (2‐3)3 (1‐3)2 (1‐2)2 (1‐2)2 (0.5‐2)

2 (1‐2)2 (1‐2)3 (3‐3)3 (3‐3)3 (3‐3)3 (3‐3)3 (3‐3)20.5 (16.8)

21.3 (16.6)

20.3 (17.1)

20.5 (16.6)

21.6 (16.3)

75.1 (21.0)

76.3 (20.6)

75.2 (22.2)

75.7 (20.6)

73.1 (25.4)

25.8 (32.8)

27.5 (33.5)

24.5 (32.9)

27.3 (32.8)

31.7 (33.9)

19.7 (7.9)

19.5 (7.9)

19.5 (7.9)

20.9 (7.2)

19.6 (7.6)

3.0 (1.0‐4.0) 3.0 (1.0‐4.5) 3.0 (1.0‐4.0) 3.0 (1.0‐4.0) 3.0 (1.0‐4.5)
2.0 (1.0‐4.0) 2.0 (1.0‐4.0) 2.0 (1.0‐4.0) 2.0 (1.0‐4.0) 2.0 (1.0‐5.0)
2.0 (1.0‐4.0) 2.0 (1.0‐4.0) 2.0 (1.0‐4.0) 2.0 (1.0‐4.0) 2.0 (1.0‐4.0)

5.0 (2.0‐6.0) 5.0 (2.5‐6.0) 5.0 (2.0‐6.0) 4.5 (2.5‐6.0) 5.0 (2.0‐6.0)
4.0 (2.0‐6.0) 5.0 (2.0‐6.0) 4.0 (2.0‐6.0) 4.5 (2.5‐6.0) 4.5 (2.0‐6.0)
4.0 (2.0‐6.0) 5.0 (2.0‐6.0) 4.0 (2.0‐6.0) 4.0 (2.0‐6.0) 4.0 (2.0‐6.0)
39 (32.5)

34 (34.0)

26 (32.5)

20 (33.3)

14 (35.0)

37 (30.8)

32 (32.0)

24 (30.0)

19 (31.7)

12 (30.0)37 (30.8)

32 (32.0)

24 (30.0)

19 (31.7)

12 (30.0)

Abbreviations: SIAS, stroke impairment assessment set; HDS-R, revised Hasegawa’s dementia scale; STEF,
simple test for evaluating hand function.
of the variables between the dressing independence ordependence groups upon admission and at one monthafter admission, the differences in all variables on all
datasets were significant, except for age and STEF on
the affected side of the 100 patient dataset (Table 2).
The results of the logistic regression with significant
variables on the comparison between groups as
Jpn J Compr Rehabil Sci Vol 11, 2020

independent variables, BBS, and STEF on theunaffected side and SIAS verticality were selected as
independent variables of the model in the dataset of
120 patients. BBS and STEF on the unaffected sideand time poststroke were selected in the dataset
containing 100 patients, and BBS and HDS-R wereselected in the dataset of 80 patients. Age, BBS, andFujita T et al.: Relationship between the number of samples and accuracy of an artificial neural network model31

Table 2. Variables with significant differences when comparing dressing independent and dependent groups at 1
month after admission.
Variables at admission
n=120

Age, Poststroke time at admission, FIM® dressing, SIAS verticality, SIAS abdominal muscle
strength, SIAS visuospatial perception, Berg balance scale, STEF affected side, STEF unaffectedside, HDS-R

n=100

Post-stroke time at admission, FIM® dressing, SIAS verticality, SIAS abdominal muscle strength,
SIAS visuospatial perception, Berg balance scale, STEF unaffected side, HDS-R

n=80

Age, Poststroke time at admission, FIM® dressing, SIAS verticality, SIAS abdominal muscle
strength, SIAS visuospatial perception, Berg balance scale, STEF affected side, STEF unaffectedside, HDS-R

n=60

Age, Poststroke time at admission, FIM® dressing, SIAS verticality, SIAS abdominal muscle
strength, SIAS visuospatial perception, Berg balance scale, STEF affected side, STEF unaffectedside, HDS-R

n=40

Age, Poststroke time at admission, FIM® dressing, SIAS verticality, SIAS abdominal muscle
strength, SIAS visuospatial perception, Berg balance scale, STEF affected side, STEF unaffectedside, HDS-R

Abbreviations: SIAS, stroke impairment assessment set; HDS-R, revised Hasegawa’s dementia scale; STEF,
simple test for evaluating hand function.
Table 3. Comparison of the performance of artificial neural network and logistic regression models.
Accuracy (%)
n=120
n=100
n=80
n=60
n=40

ANN
LRa
ANNb
LRb
ANNc
LRc
ANNd
LRd
ANNe
LRe
a

88.3
＊
80.0
84.0
75.0
85.0
＊
73.8
83.3
80.0
90.0
82.5

PPV (%)NPV (%)

83.2
72.7
82.2
＊
65.8
86.7
69.7
80.0
73.3
83.3
85.4

Sensitivity (%) Specificity (%)

91.7
＊
85.4
86.9
80.5
85.9
82.8
89.0
87.5
100.0
88.3

81.7
＊
65.8
68.3
56.7
63.3
56.7
75.0
70.0
100.0
70.0

91.4
86.7
91.0
83.6
94.3
82.7
88.5
86.0
86.7
90.0

*p < 0.05.
Abbreviations: ANN, artificial neural network; LR, logistic regression; PPV, positive-predictive value; NPV,
negative-predictive value.
aModels created by SIAS verticality, Berg balance scale, and STEF on unaffected side.
b
Models created by time poststroke, Berg balance scale, and STEF on unaffected side.
c
Models created by Berg balance scale and revised Hasegawa’s dementia scale.
dModels created by age, Berg balance scale, and STEF on unaffected side.
e
Models created by Berg balance scale, and STEF on unaffected side.
STEF on the unaffected side were selected in thedataset of 60 patients, and BBS and STEF on theunaffected side were selected in the dataset of 40
patients.
When comparing the accuracy of the created artificial
neural network and the logistic regression model in thedatasets of 120, 100, and 80 patients, the artificial neuralnetwork model exceeded the logistic regression modelin terms of classification accuracy, sensitivity, specificity,
positive predictive value, and negative predictive value.
The differences in classification accuracy, sensitivity,and negative predictive value were significant in thedataset of 120 patients, the differences in positive
predictive value were significant in the dataset of 100
patients, and the differences in classification accuracy
were significant in the dataset of 80 patients (Table 3).
However, the difference between the accuracy of theartificial neural network and the logistic regression
model was not significant in the datasets containing 40
and 60 patients.
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Fujita T et al.: Relationship between the number of samples and accuracy of an artificial neural network model

Discussion
In medical practice, many studies have createdvarious prediction models by using artificial neuralnetworks and have compared their accuracy withlogistic regression models. Although some reports
demonstrated no difference in prediction performance
between artificial neural networks and logistic
regression models [17‐22], many others concluded that
artificial neural networks are superior [23‐31]. In recent
years, a systematic review and meta-analysis on theoutcomes of trauma patients [32] also reported that
artificial neural network models have better performance
than logistic regression. Furthermore, the authors
reported that even in a small sample of 83 patients,
artificial neural networks successfully created models
with higher prediction accuracy than logistic regressionin terms of predicting the dressing independence ofstroke patients [5]. By contrast, no studies have verified
the difference in accuracy between artificial neuralnetworks and logistic regression by changing thenumber of samples, and only a few reports used a small
number of samples (i.e., 100). Therefore, the lower
limit on the number of samples needed to create a usefulprediction model by using artificial neural networks is
unknown.
Our results suggested that the lower limit on thenumber of samples to create a useful prediction modelfor dressing independence when using artificial neuralnetworks is approximately 80 and that the advantage
may be lost if the number of samples is <60. In a sample
of approximately 80 patients, the artificial neuralnetwork successfully created a model with higher
accuracy than logistic regression; this is consistent withthe result of a previous study [5]. In the dataset of 100
patients, the difference in the classification accuracy
between the artificial neural network and logistic
regression was not significant compared with that in thedatasets of 120 and 80 patients; however, the artificial
neural network exceeded the logistic regression in all
positive predictive values, negative predictive values,
sensitivities, and specificities. Sensitivity and specificity
or the positive and negative predictive values are in a
tradeoff relationship; therefore, if only one of them ishigh, it does not mean that the prediction accuracy issuperior. However, in the dataset of 100 patients, allsensitivities, specificities, positive predictive values,
and negative predictive values of artificial neuralnetworks were higher than those of the logistic
regression, in addition to the significant differences
observed in the positive predictive value. Therefore, we
believe that the prediction accuracy of the artificial
neural network model was also higher than that of thelogistic regression even in the dataset of 100 patients.
The artificial neural network exceeded all of theclassification accuracy, positive predictive value,
negative predictive value, sensitivity, and specificity inthe dataset of 60 patients; however, the values of both
Jpn J Compr Rehabil Sci Vol 11, 2020

were very similar and there was no significant difference.
We found no difference in accuracy between the twomodels in the dataset of 60 patients.
In this study, the dressing performance of the upperand lower bodies was comprehensively addressed
because we believe that the ability of stroke patients toperform a series of dressing activities without
monitoring or assistance should be carefully monitored.
However, the results of the FIM® instrument of theupper and lower body items showed that the lower bodyscored slightly lower than the upper body. This meansthat whether a series of dressing activities can be
performed independently depends largely on whether
the lower body can be independently dressed. Therefore,
the results of this study may strongly reflect theperformance of lower body dressing.
The findings of this study will be useful in thecreation of a unique model that predicts the dressing
independence at a single facility in a rehabilitation
center. In other words, when creating a prediction
model for dressing independence at a single facility
with >80 samples, an artificial neural network should
be used to improve the prediction accuracy. If thenumber of samples is ≤60, logistic regression can be
expected to create a model with the same accuracy as
an artificial neural network. Future studies shouldverify whether the results differ when performing
separate analyses between the upper and lower bodies.
Although this study focused on dressing independence,
whether the same result can be obtained with another
ADL should be confirmed. Furthermore, whether thesame results can be obtained with data from otherfacilities should also be confirmed to examine theinfluence of changing the independent variables of themodel.
Acknowledgments
This work was supported by a grant from theJapanese Society for the Promotion of Science Grantsin-Aid for Scientific Research (grant no. 18K17728).
The use of the FIM® instrument to collect data for thisresearch study was authorized and conducted inaccordance with the term of a special purpose license
granted to the licensee by Uniform Data System for
Medical Rehabilitation (UDSMR). The licensee has
not been trained by UDSMR in the use of the FIM®
instrument, and the patient data collected during thecourse of this research study has not been submitted toor processed by UDSMR. No implication is intended
that such data has been or will be subjected to thestandard data processing procedures of UDSMR orthat it is otherwise comparable with the data processedby UDSMR. FIM® is a trademark of UDSMR, adivision of UB Foundation Activities, Inc.

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