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Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior

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Background: High dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method. Methods: We collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients’ behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations. Results: Starting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively. Conclusions: Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient’s behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals.
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© Cardiovascular Diagnosis and Therapy. All rights reserved. Cardiovasc Diagn Ther 2015;5(3):219-228www.thecdt.org
Introduction
Hypertension is considered as the highest attributable risk
for mortality in the world, accounting for 16.5% of global
deaths annually (1). It is estimated that high dietary salt
intake is accountable for up to 30% of the prevalence of
hypertension (2). Furthermore, Mozaffarian et al. showed
that excessive dietary sodium intake was responsible for
1.38 million cardiovascular disease (CVD) deaths worldwide
in 2010; 45% were due to coronary heart disease (CHD),
46% due to stroke, and 9% due to other cardiovascular
complications. From the preventive point of view, He et al.
Original Article
Artificial neural network modeling using clinical and knowledge
independent variables predicts salt intake reduction behavior
Hussain A. Isma’eel1,2,3*, George E. Sakr2,4*, Mohamad M. Almedawar1,2*, Jihan Fathallah1, Torkom
Garabedian1,2, Savo Bou Zein Eddine1, Lara Nasreddine2,5, Imad H. Elhajj2,4
1Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon; 2Vascular Medicine Program, American
University of Beirut Medical Center, Beirut, Lebanon; 3Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio,
USA; 4Department of Electrical & Computer Engineering, 5Department of Nutrition & Food Sciences, American University of Beirut, Beirut,
Lebanon
*These authors contributed equally to this work.
Correspondence to: Lara Nasreddine, PhD. Department of Nutrition & Food Sciences, American University of Beirut, 11072020 Riad el Solh, Beirut,
Lebanon. Email: ln10@aub.edu.lb; Imad H. Elhajj, PhD. Department of Nutrition & Food Sciences, American University of Beirut, 11072020 Riad
el Solh, Beirut, Lebanon. Email: ie05@aub.edu.lb.
Background: High dietary salt intake is directly linked to hypertension and cardiovascular diseases
(CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their
effectiveness. We aim to compare the accuracy of an articial neural network (ANN) based tool that predicts
behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative
to the least square models (LSM) method.
Methods: We collected knowledge, attitude and behavior data on 115 patients. A behavior score was
calculated to classify patients’ behavior towards reducing salt intake. Accuracy comparison between ANN
and regression analysis was calculated using the bootstrap technique with 200 iterations.
Results: Starting from a 69-item questionnaire, a reduced model was developed and included eight
knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy
in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and
reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and
reduced models is 82% and 102%, respectively.
Conclusions: Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The
statistical model has been implemented in an online calculator and can be used in clinics to estimate the
patient’s behavior. This will help implementation in future research to further prove clinical utility of this
tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals.
Keywords: Sodium chloride; dietary; articial neural networks (ANNs); salt intake reduction; behavior prediction;
dietary intervention
Submitted Mar 03, 2015. Accepted for publication Apr 27, 2015.
doi: 10.3978/j.issn.2223-3652.2015.04.10
View this article at: http://dx.doi.org/10.3978/j.issn.2223-3652.2015.04.10
220 Isma’eel et al. Predicting salt intake reduction behavior
© Cardiovascular Diagnosis and Therapy. All rights reserved. Cardiovasc Diagn Ther 2015;5(3):219-228www.thecdt.org
showed in a meta-analysis that reducing salt intake to
6 g/person/day would reduce the incidence of stroke and
ischemic heart disease (IHD) by 24% and 18%, respectively.
Achieving this reduction through increasing awareness and
knowledge of patients is thought to help reduce the overall
burden of CVD.
Knowledge is believed to have a strong influence on
attitude which in turn denes one’s behavior (3). Numerous
studies have used regression analyses to show that nutrition
knowledge is a predictor of eating behavior for various
food groups (4,5). In particular, studies have shown that
consumers claim to eat less salt than their true intake due to
lack of knowledge on main contributors to salt in the diet
(6,7). However, the exact nature of the association between
nutrition knowledge or attitudes and dietary behavior
remains a considerably controversial topic (8).
Predicting the patients’ adherence to a low salt diet
from determining his baseline knowledge would be vital
for guiding educational interventions and in bridging the
knowledge gap. Nutrition behavior prediction algorithms
proposed thus far have been predominantly based on least
squares models (LSMs) methods of modeling applied to
observed prediction accuracy. Known limitations of LSM
might affect its applicability and accuracy in prediction
models. In particular, as the complexity of the relationship
between the dependent and independent variable increases
and is non-linear, the LSM method becomes less capable
in predicting the outcome correctly (9). The latter
complexity, we hypothesize, could be a reason behind the
controversial association between nutritional knowledge and
behavior (9). One particular outcome prediction model
gaining popularity in the clinical research eld is articial
neural network (ANN) which aims to “uncover the hidden
causal relationships between single or multiple responses
and a large set of properties” (10). This computational
model functions similarly to our central nervous system in
the sense that a node, or neuron, incorporates signals and
processes them. The complex integration of inputs follows
the multilayered matrix decision model which in turn leads
to the nal outcome (11). ANN is widely used in behavior
prediction with high accuracy, such as in predicting
customers’ behavior (12,13), intentional violations by
employees (14), and pattern of physical activity level in
children (15). Earlier attempts to use neural networks
for prediction of nutrition behavior have hypothesized
improved accuracy by neural network based algorithms and
potential impact on the prevention of CVDs (16). In theory,
ANN modelling approaches to nutrition behavior prediction
may minimize or avoid some of the limitations of the LSM
and may result in more accurate behavior prediction to
direct a more influential educational intervention. Yet, to
date, ANN modeling has not been applied in predicting salt
use behavior. Accordingly, we aimed to compare LSM and
ANN modeling using key knowledge independent variables
(KIVs) to predict salt reduction behavioral class in a high
cardiovascular risk cohort and to develop an online tool
using this model that can facilitate its implementation in
future research.
Methods
Study design
Data collection, inclusion, and exclusion criteria
We included adult patients, from both genders, with a
history of acute presentation of Hypertension, coronary
artery disease (CAD), congestive heart failure, and/or
history of Stroke/Transient Ischemic Attack admitted
to the Cardiac Care Unit. Sample size calculated was
based on recent data that around 82.2% of adults in the
Lebanese community are estimated to be aware that salt/
sodium worsens health (17). Taking this percentage into
consideration with a confidence interval of 7%, and
considering a type 1 error of 5%, a representative sample
of 115 patients out of 1,500 annual CCU admissions is
needed. Patients who agreed to participate were surveyed
using a questionnaire on knowledge, attitude, and behavior
(KAB) pertaining to salt intake. The study was approved by
the local Institutional Review board.
Study instruments
The development of the questionnaire (Supplementary 1)
was based on a thorough review of the literature and the
questions were modelled on those used in past surveys
(18-20) but culture-specic modications were introduced,
such as the examples of foods that were included
(Supplementary 1). The questionnaire was translated
to Arabic. The Arabic version of the questionnaire was
reviewed by two Arab speaking research nutritionists to
ensure that the wording of the questions was culture-
specic (17). The questionnaire was previously eld-tested
and adopted in a recent study conducted on adult Lebanese
consumers recruited from shopping centers in Beirut (17).
Patient answers were translated into numerical values to be
used in the statistical methods. The questionnaire has three
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parts: knowledge, attitude and behavior questions. The
knowledge questions are 31 objective questions that have
one correct answer. Hence, numerically, every question
was represented by either 0 (wrong answer/don’t know) or
1 (correct answer). These questions addressed familiarity
with daily salt intake requirements, as well as knowledge
about different foods and their salt content. It also tackled
knowledge on salt and its associated health hazards. The
internal consistency of the knowledge questionnaire was
previously shown to be relatively high, with the Cronbach’s
α reliability estimate being of 0.748 (17).
The attitude questions are 14 qualitative questions that
have four choices. The choices were designed to reect the
patient’s attitude towards reducing salt intake, from not
favorable attitude to favorable attitude. Numerically, every
question is represented by an integer number, ranging from
1 (not favorable) to 4 (favorable). These questions inquired
on the patient’s concern with salt levels in food, ability to
comprehend nutritional information, appropriateness of
information on food labels, incentives for reducing salt
intake, and barriers against salt reduction. The internal
consistency of the attitude questionnaire was previously
shown to be relatively high, with the Cronbach’s α reliability
estimate being of 0.724 (17).
Similarly, the behavior questions are 11 qualitative
questions that have four choices. The choices are designed
to reflect the quality of behavior from not favorable
to favorable behavior. Numerically every question is
represented by an integer number between 1 (not favorable)
and 4 (favorable). These questions tackled whether patients
actively reduce their intake and how, whether they look at
food labels and what they look for.
Twenty-four CIVs were added as real values extracted
from medical tests performed on the subject. They included
patient characteristics and laboratory values such as Blood
pressure, BMI, family history, past medical history, blood
cholesterol levels, and current medication. These values
were then introduced into the statistical learning algorithms
that were used in order to perform prediction.
Calculation of salt behavior score (SBS)
Using the 11 behavior questions, where every question
represents one IV, we were able to compute a SBS for
every subject. SBS was computed by adding up all integers
representing the 11 questions. Hence, the lowest SBS that
a subject can have is 11. It represents very unfavorable
behavior. On the other hand, the highest SBS that anyone
can achieve is 44. It represents a very favorable behavior.
SBS was computed for all subjects. The mean score of
the study sample was 29±5. Hence the behavior score
categorized patients into one of three classes: a non-
favorable class, labeled C, represented by a score less
than 26; a less favorable class, labeled B, represented by
a score between 26 and 31; and a favorable class, labeled
A, represented by a score larger than 31. The three
classes dened by this index are based on thirtiles of the
behavior score for all subjects. The lowest cutoff point
of 26 represents the 33% thirtile of the behavior score
for all subjects, while the 2nd cutoff point of 31 represents
the 66% thirtile of the behavior score of all subjects.
The choice of thirtiles is justified by the appended
questionnaire. All the behavior-related questions have
three different answers; one indicates if the behavior of the
person is very favorable, another indicates a less favorable
behavior and nally an answer that indicates unfavorable
behavior.
Data analysis
The LSM is the starting point for devising any best-tting
model. It is essential to implement LSM because it gives
an indicator about the relevance (P value) of each of the
predictors. A linear LSM is given in general by y = β’.x
Eq. [1], where x is a column vector of all predictors, β a
column vector of the coefficients associated with every
predictor and y represents the predicted risk. The LSM
algorithm nds the best vector β that ts this model.
After the data collection phase, we place the predictors
for every subject in a matrix X, where every row corresponds
to one subject, and the corresponding risks of all subjects
are placed in a column vector y. Then the coefcient vector
is computed using: β = (XTX)−1XTy. This method yields the
best linear model that ts the data. Referring to it as best
indicates that this is the model that minimizes the square
error. Non-linear models can also be considered. However,
in regression methods, there is no systematic way to know
the non-linear function that relates the input vector x to the
output y. One can try Log function or exponential function,
but the best model might be more complicated than just a
log or an exponential. The solution to this problem comes
with ANN as described in the next section.
A detailed introduction to ANN has been described by
Hagan et al. (21). In this study, a standard feed-forward
multilayer network was used. It consisted of ten input
layers and one output layer. The input layer consists of
222 Isma’eel et al. Predicting salt intake reduction behavior
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ten neurons, to which is connected all the observed IVs.
The output layer consists of 1 neuron since the output of
the network has to be a single real number representing
the predicted class. In total, we have 11 neurons: a hidden
layer containing ten neurons and an output layer containing
another neuron. The network architecture was chosen using
a standard systematic method where the number of hidden
neurons is changed incrementally, and the network that
gives the highest overall accuracy (derivation/validation)
is chosen. The transfer function in the hidden and output
layers is the tangent sigmoid function dened by
( )
fn
=+
nn
nn
ee
ee
Eq. [2].
The training of the network was done using the
Levenberg-Marquardt back-propagation algorithm. This
algorithm nds the weights that minimize the error using a
variation of Newton’s method for minimizing functions (22).
This algorithm was chosen because it is the fastest neural
networks training algorithm for moderate size networks (21)
as is the case in this study. The validation cohort was based
on 25 patients from the total sample whereas 90 patients
were used as a derivation cohort. During the training phase,
the derivation cohort was randomly split into 80% training
and 20% validation. The training was repeated 200 times
and the model that yielded the lowest error was used on the
validation set.
Reduced model (RM)
The nature and structure of the questionnaire suggests that
a possible correlation exists between different predictors.
Hence, a correlation study was performed over every part of
the questionnaire independently including all the questions
as predictors. The outcome of the ANN model is the
behavior class.
For the KIVs, a cross correlation matrix was computed.
This matrix shows the correlation between all possible
combinations of predictors. Then the correlation
coefcient R is examined. If two predictors are correlated
with R >0.5, then one of the two predictors is dropped.
This procedure was carried out over the attitude independent
variables (AIVs) as well as the CIVs. This is a standard
method used for feature reduction, it keeps the features
that have high variance and if two features have both high
variance but correlated together then one of them will be
eliminated (23).
This procedure yielded a great reduction in the number
of predictors used. This model is referred to later on in
the paper as reduced model. We will also present in the
results section, a comparison between the RM and the full
model (FM).
LSM versus ANN performance comparisons
To compare the performances of the LSM and ANN in
predicting the behavior class, the following method was
used. The data was split randomly as 80% derivation cohort
(92 subjects in total) and 20% validation cohort (23 subjects
in total). Next a 200-iteration bootstrap was performed.
In every iteration, 92 subjects were picked randomly with
repetition from the derivation cohort (one subject might
appear more than once). These 92 subjects were used to
derive the optimal model. This model was then used to get
the accuracy on the validation cohort (percentage of subjects
that were correctly classied). It is important to note that
the validation cohort was never used during the derivation
phase. Finally, the average prediction accuracy over the 200
iterations, which represents the number of subjects that
were correctly classified by our model, is used to evaluate
the performance of the LSM model and the ANN model.
Results
Our cohort consisted of 115 high-risk patients (mean age in
years: 60.63±15.39) including 74 (64.3%) men (Table 1). The
mean BMI was 31.30±22.39 kg/m2. Of the study sample,
74.6% were hypertensive, 43% were diabetic, 32.5% had a
history of angina, 32.7% had a history of congestive heart
failure, and 34.2% had a history of myocardial infarction.
A history of coronary artery bypass graft (CABG) and
percutaneous coronary intervention (PCI) were noted in
28.1% and 38.6% of the sample, respectively, while 30.7%
underwent PCI during the current visit. A family history of
CAD, hypertension, and diabetes was reported in 26.3%,
24.6%, and 25.4% of the sample, respectively. Two thirds of
the participants were non-smokers (62.3%).
From the bootstrap analysis with 200 iterations we
showed that using the FM variables to predict behavioral
class, the highest accuracy achieved by LSM in the validation
cohort was 40% CI (56-60%) (Table 2). This was attained
from including knowledge and attitude questions only.
The LSM model obtained is given by the following
formula:
Class =1.34+0.15.q9−0.14.q10b+0.38.q10c+0.07.q10d
−0.03.q11−0.01.q13+0.24.q14a+0.06.q14n−0.11.q21+0.03.
q22a−0.14.q25-0.6.q26+0.8.q27−0.01.q30+0.07.q32.
Where q9, q10, q11, q13 and q14 are equal 1 if the
patient answers the corresponding question correctly and
0 otherwise, and q21, q22, q25, q26, q27, q30, q32 are
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the attitude scaled numbers calculated from the patient’s
answers to the corresponding questions (1 being not
favorable to 4 being favorable).
Furthermore, the cross correlation study has shown
that RM can be described using only 8 knowledge, 7
attitude, and 5 clinical questions instead of a total of 31,
14, and 24 questions, respectively. The eight remaining
knowledge questions inquire about the effect of the salt/
sodium on health, whether there is a causal relationship
between salt and stroke, osteoporosis, and uid retention,
the recommended maximum daily intake of salt, the
relationship between salt and sodium and knowledge of
salt/sodium level in Bread. The seven remaining attitude
questions cover the comprehensibility of nutrition
information on sodium present on food labels, whether
patients were concerned about artificial flavors in food
products, the importance of reducing the amount of salt and
sodium added to food and the amount of processed foods
consumed. It also inquired on the worst possible scenario
that could result from excessive salt intake, and who the
responsible party is in terms of reducing salt intake per
individual. The five remaining CIVs measure systolic and
diastolic blood pressure, pulse, smoking status, and medical
history of hypertension. In the RM bootstrap analysis, the
LSM needed knowledge, attitude and clinical variables to
attain the highest accuracy of 34% CI (17-47%) in correctly
predicting behavioral class (Table 3).
Alternatively, Table 2 shows that ANN outperforms LSM
Table 1 Key sample demographics compared to the Lebanese
population
Characteristics N (%) Lebanese population
(2009) %
Age (years)
19-30 6 (5.21) 16.8
31-40 5 (4.34) 12.8
41-50 14 (12.17) 12.9
51-60 34 (21.57) 10.9
>60 56 (48.69) 11.2
Gender
Male 74 (64.34) 49.02
Female 41 (35.66) 50.98
Health related field of study
No 103 (89.6)
Yes 12 (10.4)
Educational level
Intermediate or lower 40 (34.8) 38.2
High school or
technical degree
25 (21.7) 27.7
University 50 (43.5) 34.1
Crowding index (CrI)
<1 person/room 90 (78.26) 37.6
≥1 person/room 25 (21.74) 62.4
, percentages for Lebanese population demographics were
obtained from references (24-27).
Table 2 ANN FM vs. LSM FM predicting behavior class in the sample (n=115)
Independent variables ANN accuracy 95% CI [%] LSM accuracy 95% CI [%] Relative increase in accuracy
of ANN over LSM (%)
Derivation Validation Derivation Validation Validation
Knowledge 60 [56-64] 58 [55-62] 71 [48-86] 35 [17-52] 65.7
Attitude 60 [55-64] 60 [57-66] 47 [35-60] 30 [17-43] 100.0
Clinical 72 [66-79] 61 [56-66] 67 [51-82] 28 [8-43] 117.0
Knowledge + attitude 76 [73-80] 62 [60-66] 89 [75-98] 40 [56-60] 55.0
Knowledge + clinical 86 [82-91] 62 [59-67] 99 [96-100] 38 [17-56] 63.1
Attitude + clinical 83 [80-89] 63 [60-66] 82 [64-97] 35 [8-56] 80.0
Knowledge + attitude + clinical 83 [79-90] 66 [62-69] 100 [100-100] 38 [21-52] 73.6
, the 95% confidence intervals (CIs) on the estimates of mean absolute error were computed by bootstrapping with 200 iterations.
ANN, artificial neural networks; FM, full model; LSM, least-square models.
224 Isma’eel et al. Predicting salt intake reduction behavior
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in any possible IV combination on the validation cohort
and in some cases, ANN has double the accuracy of LSM.
The highest validation accuracy was recorded at 66% for
the knowledge + attitude + clinical in ANN FM, compared
to 40% CI (56-60%) accuracy of the best LSM FM, using a
set of 46 knowledge and attitude questions. The validation
accuracy over that of derivation is noted to be comparable
between the two algorithms. However, the average relative
increase in accuracy between ANN and LSM over all
possible IV combinations on the validation data is 82%.
Similarly, in Table 3 we demonstrate the power of ANN
compared to LSM as it outperforms it in all possible
IV combination using the reduced model variables. On
average, the relative increase in accuracy of ANN over
LSM in all reduced models combined is 102%. The most
accurate LSM RM is the one using the combination of
knowledge, attitude and clinical set of 20 questions at 34%
CI (17-47%), whereas the best ANN RM is the one using 8
knowledge questions only at 62% CI (58-67%). The ANN
model obtained is given by the formula in Supplementary 2.
To illustrate further, Figure 1 compares the best FM
using each method while Figure 2 compares the best RM
using each method. Figure 1 shows that using ANN, the
behavior class of 66% of the patients was correctly predicted
whereas 34% were misclassified by one behavioral class.
No patients were grossly misclassied, that is, misclassied
by 2 behavioral classes. On the other hand, using LSM, the
behavior class of only 38% of the patients was correctly
Table 3 ANN RM vs. LSM RM predicting behavior class in the sample (n=115)
Independent variables ANN accuracy 95% CI [%] LSM accuracy 95% CI [%] Relative increase in accuracy
of ANN over LSM (%)
Derivation Validation Derivation Validation Validation
Knowledge 50 [45-55] 62 [58-67] 40 [26-56] 28 [17-43] 121
Attitude 46 [42-51] 58 [51-63] 37 [27-48] 29 [13-43] 100
Clinical 47 [45-50] 60 [56-67] 39 [26-51] 26 [13-39] 130
Knowledge + attitude 60 [57-64] 58 [55-62] 50 [37-64] 33 [17-47] 75.5
Knowledge + clinical 61 [57-67] 60 [56-65] 50 [34-62] 28 [13-47] 114
Attitude + clinical 50 [43-55] 58 [55-65] 45 [30-63] 30 [13-47] 93.3
Know+ attitude + clinical 54 [50-58] 61 [58-66] 54 [42-69] 34 [17-47] 79.4
, the 95% confidence intervals (CIs) on the estimates of mean absolute error were computed by bootstrapping with 200 iterations.
ANN, artificial neural networks; RM, reduced model; LSM, least-square models.
Correctly
classified
Grossly
misclassified
Misclassified
Class error
ANN
LSM
% of subjects
70
60
50
40
30
20
10
0
Correctly
classified
Grossly
misclassified
Misclassified
ANN
LSM
% of subjects
70
60
50
40
30
20
10
0
Class error
Figure 1 Accuracy and error in predicting patient behavior class
using the best ANN FM vs. LSM FM (n=115). ANN, artificial
neural networks; FM, full model; LSM, least-square models.
Figure 2 Accuracy and error in predicting patient behavior class
using the best ANN RM vs. LSM RM (n=115). ANN, artificial
neural networks; RM, reduced model; LSM, least-square models.
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predicted whereas 40% were misclassied by one behavioral
class, and 22% were grossly misclassied.
Similarly, Figure 2 shows that using ANN, the behavior
class of 62% of the patients was correctly predicted whereas
38% were misclassied by one behavioral class. No patients
were grossly misclassified, that is, misclassified by two
behavioral classes. Using LSM, the behavior class of only
34% of the patients was correctly predicted whereas 34%
were misclassified by one behavioral class, and 32% were
grossly misclassied.
Discussion
The ANN-based algorithm provided superior accuracy for
predicting patient behavior towards salt intake reduction
over the LSM-based model, with an average relative
increase in accuracy of 82% between the best ANN RM
over the best LSM RM, while in FM, the relative increase in
accuracy was 65%. In FM, the ANN-based model requiring
all IVs achieved the highest validation accuracy at 66% CI
(62-69%). In ANN RM, only eight KIVs were required to
achieve the highest validation accuracy at 62% CI (58-67%).
While only 34% were misclassied using ANN FM, 40%
were misclassied and 22% were grossly misclassied using
LSM FM.
To our knowledge, no previous study has tried to predict
salt intake behavior using an ANN-based model. However,
a study by Borowiec et al. utilized neural networks to
predict food type purchase patterns based on a set of
independent variables including socio-demographics and
the economic capability of buying thirteen different food
product categories. Households were split into two clusters
or nutrition status groups. The first can buy only basic
food categories while the other group can buy all food
categories. The obtained classification error rate for the
best neural network was lower than the corresponding error
rate for both Discriminant analysis and Logistic regression,
and hence ANN improved the classification accuracy and
outperformed statistical methods (16). Moreover, although
both LSM and ANN can be used as non-linear regression
or classification methods, the main drawback of LSM
is the need to try out several non-linear models such as
logarithmic and exponential, among others. Hence, only
trial and error can tell which model fits best. However,
the structure of ANN combined with its optimization
algorithms will learn from the data the best model to use
and its parameters (21).
Furthermore, though our ANN model improved the
rate of correctly classied individuals, nearly 1/3 remained
misclassified (none were grossly misclassified) (Figure 2).
To contextualize this nding we compare this performance
to other classification tools commonly used in cardiology.
For example, the Heart Score model and coronary
calcium score, both used to assess cardiovascular risk, have
signicant rate of discordance. Among those deemed as high
risk by the Heart Score system in low-risk countries, only
17% would be classified as high risk by coronary calcium
scoring (28). Similarly, the thrombolysis in myocardial
infarction (TIMI) score, a commonly used scoring tool
to risk stratify patients presenting with acute coronary
syndrome, was found to have a short term AUC of 0.66 (95%
CI, 0.64-0.68) and 0.73 (95% CI, 0.69-0.78) in derivation
and validation cohorts respectively (29). This indicates that
nearly 27-34% of individuals were misclassied. The above
indicates that our ANN model’s performance is within the
same range of other very commonly used models in the
field of cardiology. However, because of only 66% level
of accuracy, future studies to further improve this tool are
warranted for it to become applicable in the clinical setting.
The behaviour classification model we followed was
inspired by prior nutrition studies (4,30). Sharma et al.
used multiple logistic regression analysis to show that
nutrition knowledge was a strong predictor of eating
behavior for all food groups except fruits and vegetables.
This was done by creating a nutrition knowledge score and
an eating behavior score of recommended servings per day
of food types as dichotomous variables; i.e., correct and
incorrect knowledge and behavior (4). A systematic review
examined the relationship between nutrition knowledge
and dietary intake in adults, and showed that a higher
intake of fruit and vegetables was associated with higher
nutrition knowledge (30). Adopting this approach, we
believed, would provide meaning to the number provided
for the score directly. This is instead of having to take the
score achieved and then refer back to the cut-points to see
where an individual’s score is relative to the distribution
of scores. Ideally, this should be veried by a prospective
study against 24-hour urine sodium, which will be further
discussed in a subsequent section.
ANN FM had prediction accuracy higher by 4% than
ANN RM. In terms of clinical applicability, a loss of 4%
accuracy in determining patient behavioral class when
using eight questions in ANN RM instead of 69 questions
in ANN FM may be considered a worth loss in favor of
wider applicability secondary to decreasing the number
of questions. The average relative increase in accuracy
226 Isma’eel et al. Predicting salt intake reduction behavior
© Cardiovascular Diagnosis and Therapy. All rights reserved. Cardiovasc Diagn Ther 2015;5(3):219-228www.thecdt.org
between ANN and LSM over all possible IV combinations
on the validation data is 80%. This shows the power of
ANN for prediction over the standard LSM.
Implications for research and practice
Working with high-risk individuals to reduce their salt
intake through raising their awareness about the health
hazard of salt and how to cut it down is currently being
practiced. However, having a tool that can predict a
patient’s behavior after an awareness raising activity
can potentially identify the efficacy of this activity, and
accordingly help modify it to improve it. At cardiac care
units in general, including ours, dietary consultants—
or other healthcare workers—meet with the patients and
advise them about salt reduction. This is also provided for
outpatients as part of preventive measures. Using efcacy
assessing tools such as the one in hand, we can identify
whether such efforts are possibly leading to an outcome
(adopting salt intake reducing behaviors in this situation)
or not. This will ensure proper allocation of resources,
dietary consultants’ time and effort, and avoid burdening
our patients with inefficient interventions. At a larger
scale, this tool can potentially be used to gauge consumer
responsiveness to public media campaigns with the advent
of being concise and rapid.
It is well known that the best outcome measure of a salt
reduction intervention would be to perform a 24-hr urine
collection of sodium. However, this is well known to be
cumbersome for the patients. Therefore, having this tool
as a surrogate of a behavioral change may be a reasonable
approach, although certainly not ideal. To our knowledge
neither the original survey, nor our culturally adopted
version, have been cross validated against 24-hr urinary
sodium. This is a short-coming that needs to be addressed
in a future study. However, the results of this survey have
been accepted as a measure of behavior in literature before
(18-20), and our ANN analysis is a secondary analytical
modication based on this.
Salt awareness level tool (SALT)
To facilitate the utilization of the ANN model, the SALT
was developed in order to create an accessible interface to
calculate the behavior class of a patient from a few questions
as illustrated below in a screenshot of SALT (Figure S1).
SALT gives the ability for the user to input eight KIVs and
ve CIVs and use them for prediction with 60% accuracy.
The software was developed using C# and is also be
available as an online calculator at http://www.aub.edu.lb/
fm/vmp/research/Documents/ann-salt.htm.
Conclusions
ANN based model using knowledge and clinical variables
predict salt intake reduction behavior with superiority over
all possible LSM models. A minor loss in accuracy when
using ANN reduced model over ANN FM is insignicant
when compared to the practicality offered by the RM-
based tool based. Because of only 66% level of accuracy,
future studies to further improve this accuracy level are
warranted for it to become applicable in the clinical setting.
Furthermore, validation and improvement of the accuracy
of the tool using larger cohorts of different health and
ethnic backgrounds and against 24-hr urinary sodium is
required to achieve its full potential and benet in clinical
and public health interventions.
Limitations
Despite proving the superiority of ANN over LSM in all
models, 34% of the patients were misclassified using the
best ANN model, which can be attributed to the small
sample size used to conduct this study. Moreover, our
cohort consisted of high-risk patients in the CCU, which
might question the applicability of the tool on everyday
patients who visit their physician’s clinic or even on the
general population. Accordingly, improving the accuracy
of the model will require implementing the derivation and
validation on larger cohorts, and from different ethnic and
health backgrounds to validate the tool across different
nations and societies. Importantly, the original survey used
and the new tool need to be cross-validated against 24-hr
urine sodium to ensure behavioral class is correlated with
Na intake. The issue of validating against 24-hr urine
sodium is crucial for this model to become utilized in clinics
in general and in particular HTN specialized clinics.
Acknowledgements
We would like to acknowledge Mrs. Laila Al-Shaar for
conducting the sample size calculation and descriptive
statistical analysis of the data.
Funding: Faculty of Medicine, American University of
Beirut, Beirut, Lebanon.
Disclosure: The authors declare no conict of interest.
227
Cardiovascular Diagnosis and Therapy, Vol 5, No 3 June 2015
© Cardiovascular Diagnosis and Therapy. All rights reserved. Cardiovasc Diagn Ther 2015;5(3):219-228www.thecdt.org
References
1. Friedlander Y, Siscovick DS, Weinmann S, et al. Family
history as a risk factor for primary cardiac arrest.
Circulation 1998;97:155-60.
2. Joffres MR, Campbell NR, Manns B, et al. Estimate of the
benets of a population-based reduction in dietary sodium
additives on hypertension and its related health care costs
in Canada. Can J Cardiol 2007;23:437-43.
3. Fabrigar LR, Petty RE, Smith SM, et al. Understanding
knowledge effects on attitude-behavior consistency: the
role of relevance, complexity, and amount of knowledge. J
Pers Soc Psychol 2006;90:556-77.
4. Sharma SV, Gernand AD, Day RS. Nutrition knowledge
predicts eating behavior of all food groups except fruits
and vegetables among adults in the Paso del Norte region:
Qué Sabrosa Vida. J Nutr Educ Behav 2008;40:361-8.
5. Harnack L, Block G, Subar A, et al. Association of cancer
prevention-related nutrition knowledge, beliefs, and
attitudes to cancer prevention dietary behavior. J Am Diet
Assoc 1997;97:957-65.
6. Claro RM, Linders H, Ricardo CZ, et al. Consumer
attitudes, knowledge, and behavior related to salt
consumption in sentinel countries of the Americas. Rev
Panam Salud Publica 2012;32:265-73.
7. Arcand J, Mendoza J, Qi Y, et al. Results of a national
survey examining Canadians' concern, actions, barriers,
and support for dietary sodium reduction interventions.
Can J Cardiol 2013;29:628-31.
8. Morton JF, Guthrie JF. Diet-related knowledge, attitudes,
and practices of low-income individuals with children in
the household. Family Economics and Nutrition Review
1997;10:2.
9. Ugrinowitsch C, Fellingham GW, Ricard MD.
Limitations of ordinary least squares models in
analyzing repeated measures data. Med Sci Sports Exerc
2004;36:2144-8.
10. Zou J, Han Y, So SS. Overview of articial neural
networks. Methods Mol Biol 2008;458:15-23.
11. Reingold E. Articial Neural Networks. In: Articial
Intelligence Tutorial Reviewed. University of Toronto
Mississauga, 1999. Available online: http://psych.utoronto.
ca/users/reingold/courses/ai/
12. Zheng B, Thompson K, Lam SS, et al. eds. Customers'
Behavior Prediction Using Articial Neural Network.
Industrial and Systems Engineering Research Conference;
2013; Puerto Rico: Xerox Innovation Group. Available
online: http://www.iienet2.org/uploadedFiles/IIE/
Community/Technical_Societies_and_Divisions/SEMS/
Abstract_909.pdf
13. Tusche A, Bode S, Haynes JD. Neural responses to
unattended products predict later consumer choices. J
Neurosci 2010;30:8024-31.
14. Zhang Z, Vanderhaegen F, Millot P. Prediction of Human
Behaviour Using Articial Neural Networks. Lecture
Notes in Computer Science 2006;3930:770-9.
15. Trost SG, Wong WK, Pfeiffer KA, et al. Articial neural
networks to predict activity type and energy expenditure in
youth. Med Sci Sports Exerc 2012;44:1801-9.
16. Borowiec A, Fronczyk K, Macukow B, et al. Can Neural
Networks Be Used to Dene the Rules of Cardiovascular
Disease Prevention in the Nutrition Domain? Advances in
Soft Computing 2003;19:450-5.
17. Nasreddine L, Akl C, Al-Shaar L, et al. Consumer
knowledge, attitudes and salt-related behavior in
the Middle-East: the case of Lebanon. Nutrients
2014;6:5079-102.
18. 2007 survey of Australian consumer awareness and
practices relating to salt. ustralia: The George Institute for
International Health 2007. Available online: http://www.
awash.org.au/wp-content/uploads/2012/10/AWASH_
ConsumerSurveyReport_2007_05_15.pdf
19. Wyllie A, Moore R, Brown R. Salt Consumer Survey.
MAF Technical Paper No: 2011/9, Prepared for NZFSA
by Phoenix Research. ISBN 978-0-478-37559-6 (online),
ISSN 2230-2794 (online), March 2011, Ministry of
Agriculture and Forestry. Available online: http://www.
foodsafety.govt.nz/elibrary/industry/salt-survey.pdf
20. Papadakis S, Pipe AL, Moroz IA, et al. Knowledge,
attitudes and behaviours related to dietary sodium among
35- to 50-year-old Ontario residents. Can J Cardiol
2010;26:e164-9.
21. Hagan M, Demuth H, Beale M. eds. Neural network design,
1st ed. Boston, MA: PWS Publishing Company, 1996.
22. Scales LE. eds. Introduction to Non-Linear Optimization.
Ann Arbor: Springer Verlag Gmbh, 1985.
23. Jolliffe IT. eds. Principal Component Analysis. New York:
Springer Science & Business Media, 2013.
24. Central Administration and Statistics Population Statistics,
2009. Available online: http:// www.cas.gov.lb/index.php/
demographic-and-social-en/population-en. (Accessed on
28th May, 2014).
25. Economic and Social Commission for Western Asia. The
Demographic Prole of Lebanon. Available online: http://
www.escwa.un.org/popin/members/lebanon.pdf. (Accessed
on 13th June, 2014).
228 Isma’eel et al. Predicting salt intake reduction behavior
© Cardiovascular Diagnosis and Therapy. All rights reserved. Cardiovasc Diagn Ther 2015;5(3):219-228www.thecdt.org
26. Naja F, Nasreddine L, Itani L, et al. Dietary patterns
and their association with obesity and sociodemographic
factors in a national sample of Lebanese adults. Public
Health Nutr 2011;14:1570-8.
27. Demographic, socioeconomic, dietary and physical activity
determinants of obesity in a large nationally representative
sample of the Lebanese adult population. Available online:
http://etheses.dur.ac.uk/7321/
28. Diederichsen AC, Mahabadi AA, Gerke O, et al. Increased
discordance between HeartScore and coronary artery
calcication score after introduction of the new ESC
prevention guidelines. Atherosclerosis 2015;239:143-9.
29. D'Ascenzo F, Biondi-Zoccai G, Moretti C, et al. TIMI,
GRACE and alternative risk scores in Acute Coronary
Syndromes: a meta-analysis of 40 derivation studies on
216,552 patients and of 42 validation studies on 31,625
patients. Contemp Clin Trials 2012;33:507-14.
30. Spronk I, Kullen C, Burdon C, et al. Relationship between
nutrition knowledge and dietary intake. Br J Nutr
2014;111:1713-26.
Cite this article as: Isma’eel HA, Sakr GE, Almedawar MM,
Fathallah J, Garabedian T, Eddine SB, Nasreddine L, Elhajj IH.
Articial neural network modeling using clinical and knowledge
independent variables predicts salt intake reduction behavior.
Cardiovasc Diagn Ther 2015;5(3):219-228. doi: 10.3978/
j.issn.2223-3652.2015.04.10
Supplementary 1
Knowledge, attitude, and behavior (KAB) questionnaire used in data collection
Knowledge, attitudes and behaviors related to sodium intake of Lebanese adults
Recruitment place:
Subject:
Patient
1. Sex
Male
Female
2. What is your age? (Years)
‘19-30’
‘31-40’
‘41-50’
‘51-60’
‘61 plus’
3. Where do you live? (Governorates) (Please tick one box only)
Beirut
Mount Lebanon
North
South
Bekaa
Nabatieh
4. Have you ever or are you specialized in a health-related major (Biomedical, Nutrition, Food science, Medicine, Public
Health, and Nursing)? (Please tick one box only)
Yes, specify: _________________
No
5. Which of the following best describes your highest level of education? (Please tick one box only)
Intermediate or lower
High school
Technical degree
University bachelor’s degree (BS) or higher (Master or PhD)
6. What type of school did you attend? (Please tick one box only)
Private school
Public school
7. How many rooms are there in your house (excluding bathrooms, kitchen, balcony and garage)? _________________
8. How many people live in your house (excluding newborn infant)?_________________
9. Which of these best describes what you think is the effect of the salt/sodium in your diet? (Please tick one box only)
Improves your health
Has no effect on health
Worsens your health
Don’t know
10. Do you think these health problems can be caused or aggravated by salty foods? (For each problem please select yes, no or
don’t know)
Yes No Don’t know
□ □ High blood pressure
□ □ Stroke
□ □ Osteoporosis
□ □ Fluid retention
□ □ Heart attacks
□ □ Stomach cancer
□ □ Kidney disease
□ □ Memory/concentration problems
□ □ Asthma
□ □ Headaches
11. What is the maximum daily amount of salt recommended for adults? (Please tick one only)
3 grams (½ teaspoonful)
6 grams (1 teaspoonful)
9 grams (1 ½ teaspoons)
12 grams (2 teaspoons)
15 grams (2 ½ teaspoons)
Don’t know
12. How do you think your daily salt intake compares to the optimal amount recommended? (Please tick one only)
More than the maximum recommended
About the maximum recommended
Less than the maximum recommended
Don’t know
13. Which of the following statements best describes the relationship between salt and sodium? (Please tick one only)
They are exactly the same
Salt contains sodium
Sodium contains salt
Don’t know
14. Below is a list of everyday foods. For each please indicate whether you consider these foods to be: high, medium or low in
terms of salt/sodium content. (Please tick one box for each food)
High Medium Low Don’t know
Bread
Manaesh
Traditional pies
Pizza
Rice
Cheese
Milk
Pear
Vegetables ragouts
French fries
Sandwiches (e.g. shawarma, fajita, hamburger)
Soya sauce
Fresh Carrot
Ketchup
Salad dressings
Roasted nuts
Sausages and hot dogs
15. Which of the following do you think is the main source of salt in the diet of Lebanese people? (Please tick one only)
Salt added during cooking
Salt added at table
Salt in processed foods such as breads, cured meats, canned foods and takeaway
Salt from natural sources such as vegetables and fruits
Don’t Know
16. How often do you check food content labels when you are shopping? (Please tick one only)
Often
Sometimes
Never
I never do grocery shopping therefore this question is irrelevant
17. Does what is on the food content label affect whether or not you purchase a food item? (Please tick one only)
Often
Sometimes
Never
I don’t do grocery shopping therefore this question is irrelevant
18. How often do you look at the salt/sodium content on food labels when you are shopping? (Please tick one only)
Often
Sometimes
Never
I never do grocery shopping therefore this question is irrelevant
19. How often does the salt/sodium content shown on the food label affect whether you purchase a product? (Please tick one only)
Often
Sometimes
Never
I never do grocery shopping therefore this question is irrelevant
20. What information on the food package do you use to determine how much salt is in the product?
The sodium level in the nutrition information panel
The ingredients list
Claims for low or reduced salt on the pack
Other (specify): _________________
Don’t know
21. Do you think present nutrition information on sodium is comprehensible? (Please tick one only)
Yes
No
22. Are you concerned about these aspects of the food you eat? (Please tick ‘yes’ or ‘no’ for each option)
Yes No
Articial avours
Articial colours
Salt/sodium
Sugar
Energy (calories)
Saturated fat
23. Do you do any of the following? (Please tick one box for every question)
Often Sometimes Never Not applicable
□ □ Add salt during cooking
□ □ Add salt at the table
□ □ Try to buy ‘low salt’ foods
□ □ Try to buy ‘no added salt’ foods
24. Are you cutting down on the amount of salt you eat? (Please tick one only)
Yes
No
Don’t know
If yes, why are you cutting down on salt?
I have been told to by a doctor/other health professional
Another family member has been told to
Because it’s bad for you
Because I am on a diet
To help lower my blood pressure
Because I have health problems
To reduce my risk of a heart attack or stroke
I don’t like the taste of it
Saw an advert/article about it/something on TV
Trying to eat more healthily
Other (specify): _________________
Don’t know
If no, why aren’t you cutting down on salt?
I recently cut back and don’t need to cut back any further
I didn’t know I should
I eat a healthy diet and know I’m not eating too much salt
I’m not concerned by it
I haven’t been told to cut salt from my diet
No particular reason, hadn’t really thought about it
You need to eat salt to stay healthy
I don’t have too much salt in my diet
I don’t add salt to my food (anymore)
I don’t eat food high in salt
Other (specify): _________________
Don’t know
25. Reducing the amount of salt you add to foods is denitely important to you. (Please tick one only)
Strongly disagree
Disagree
Neither agree nor disagree
Agree
Strongly agree
26. Reducing the amount of processed foods (e.g., breads, cured meats, canned foods and takeaway) you eat is definitely
important to you. (Please tick one only)
Strongly disagree 1
Disagree2
Neither agree nor disagree3
Agree4
Strongly agree5
27. Reducing your sodium intake is denitely important to you (Please tick one only)
Strongly disagree 1
Disagree2
Neither agree nor disagree3
Agree4
Strongly agree5
28. What would motivate you to reduce your salt intake? (Please tick one only)
A dramatic change in health status 1
If my doctor advised it2
If family members or friends advised it 3
Other (specify): _________________ 4
29. What are the barriers against decreasing your salt intake? (Please tick one only)
It tastes good 1
I am not concerned with decreasing my salt intake 2
I don’t know which foods to avoid 3
Other (specify): _________________ 4
30. What is the most frightening thing that could happen if you eat too much salt? (Please tick one only)
Nothing bad will happen
I could have a heart attack or stroke
My blood pressure will go up
Other (specify): _________________
31. Where do you get your health information from? (Please tick one only)
My doctor
My family and friends
The internet
The media (specify): television radio newspapers magazines other:_________________
Other (specify): _________________
32. If excess salt/sodium in the diet were known to cause a serious disease who do you think should be MOST responsible for
helping you reduce the salt/sodium you eat? (Please tick one only)
The government (public health campaign)
Companies that make or sell foods with salt in them (food industry)
Your doctor
Yourself
33. Have you been previously advised by a physician, nurse or dietitian about the risks of a salt-rich diet and the need to
moderate salt intake? (Please tick one only)
Yes
No
Cannot remember
34. Have you been approached by a dietitian during your CCU stay? (Please tick one only)
Yes
No
The ANN model obtained based on bootstrap analysis with 200 iterations
To predict the behavioral class using ANN, the following procedure must be followed.
First you form the vector P by the following components:
P= (q9, q10b, q10c, q10d, q11, q13, q14a, q14n, sbpn, dbpn, pulsen, smk, htn)
Where q9, q10b, q10c, q10d, q11, q13, q14a, q14n are equal to 1 if the patient answers the corresponding question correctly
and −1 otherwise.
Smk is 1 if the patient is smoker and −1 otherwise.
Htn is 1 is the patient has a history in hypertension and −1 otherwise.
Sbpn is the normalized systolic blood pressure given by: sbpn=0.0123.sbp−1.1595
dbpn is the normalized diastolic blood pressure given by: dbpn=0.0263.dbp−1.7895
pulsen is the normalized pulse rate given by: pulsen=0.0125.pulse−1.2727.
Where sbp, dbp and pulse are the corresponding non normalized values.
The output n1 of the rst layer is given by:
N1=p.A1+b1.
Where A1 is shown in Figure S2.
And b1 is the following vector:
B1= (1.6118, −1.5348, −0.895, −0.85492, 0.29956, −0.27295, 0.43393, 0.92673, −1.2202, −1.6814)
The output N1 is then sent to the tansig activation function which is described earlier to get the nal output A1 of layer1 as:
A1=tansig(N1)
The next step is to send A1 into the last layer of the network. The output of the second layer is calculated as follows:
N2= A1.B +0.489
Where B is following column vector:
B = (−0.898, 0.090858, −0.0062721, 0.076167, −0.3859, −1.3466, 0.4678, 0.56129, 0.35015, 0.39472)T
Then the nal output A2 is obtained by:
A2= tansig (N2)+2
If A2<1.5 then the patient is classied as unfavorable.
If 1.5<A2<2.5 then the patient is classied as less favorable
If A2>2.5 then the patient is classied as favorable.
Supplementary 2
Figure S1 Salt awareness level tool (SALT) online calculator.
0.452 −0.562 −0.366 −0.389 −0.168 −0.058 1.150 −0.125 −0.621 0.648 0.245 −0.666 0.105
0.098 0.073 −0.571 0.526 −0.344 −0.601 −0.220 −0.606 −0.296 −0.483 0.607 0.299 0.264
0.192 −0.316 0.462 0.073 −0.225 −0.490 −0.718 −0.269 0.685 0.634 0.447 0.651 0.391
0.054 0.198 −0.754 0.381 −0.517 −0.504 −0.082 −0.319 −0.252 −0.069 0.597 −0.058 0.739
−0.008 −0.188 −0.666 0.040 0.648 0.635 −0.548 −0.460 −0.091 −0.608 −0.724 −0.359 −0.226
−0.370 −1.085 0.046 0.366 0.025 −0.981 0.143 −0.885 −0.369 0.304 −0.171 −0.710 0.655
0.295 −0.363 0.510 −0.294 0.107 −0.704 0.162 1.166 0.466 −0.390 −0.090 −0.672 −0.211
0.514 −0.288 0.122 0.359 −0.093 −0.443 −0.318 0.697 −0.623 −0.158 −0.443 0.290 −0.887
−0.276 0.959 −0.428 0.460 −0.050 −0.157 0.473 0.162 −0.514 −0.471 −0.139 0.456 0.811
−0.023 0.878 0.495 0.664 −0.468 −0.484 −0.431 −0.308 −0.114 −0.510 0.445 −0.436 −0.421
Figure S2 Matrix for A1.
ResearchGate has not been able to resolve any citations for this publication.
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Chapter
The primary objective of the work was to check the relation between household socio-economic characteristics and the corresponding food purchasing capabilities. The analysis has been based on the data collected in the national survey “Social Diagnosis 2000”. In order to search for possible dependencies between variables gathered in the survey, different classification methods have been applied. Statistic methods of logistic regression analysis and discriminant analysis have been applied to model the discussed relation. However, only limited prediction efficiency has been observed. Therefore, neural networks-based methods have been applied. Evolutionary construction of multilayer perceptrons has been used to select both network architectures and weights. The method developed and tested previously on numerous prediction and classification problems has been used to provide classification models for the data discussed. Multilayer perceptrons have been shown to provide more precise classification models. Results of the network construction are presented together with final discussion.
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In this paper, customer restaurant preference is predicted based on social media location check-ins. Historical preferences of the customer and the influence of the customer's social network are used in combination with the customer's mobility characteristics as inputs to the model. As the popularity of social media increases, more and more customer comments and feedback about products and services are available online. It not only becomes a way of sharing information among friends in the social network but also forms a new type of survey which can be utilized by business companies to improve their existing products, services, and market analysis. Approximately 121,000 foursquare restaurant check-ins in the Greater New York City area are used in this research. Artificial neural networks (ANN) and support vector machine (SVM) are developed to predict the customers' behavior regarding restaurant preferences. ANN provides 93:13% average accuracy across investigated customers, compared to only 54:00% for SVM with a sigmoid kernel function.
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The European HeartScore has traditionally differentiated between low and high-risk countries. Until 2012 Germany and Denmark were considered to be high-risk countries but have now been defined as low-risk countries. In this survey we aim to address the consequences of this downgrading. A screening of 3932 randomly selected (mean age 56 years, 46% male) individuals from Germany and Denmark free of cardiovascular disease was performed. Traditional risk factors were determined, and the HeartScore was measured using both the low-risk and the high-risk country models. A non-contrast Cardiac-CT scan was performed to detect coronary artery calcification (CAC). Agreement of HeartScore risk groups with CAC groups was poor, but higher when applying the algorithm for the low-risk compared to the high-risk country model (agreement rate: 77% versus 63%, and weighted Kappa: 0.22 versus 0.15). However, the number of subjects with severe coronary calcification (CAC score ≥400) increased in the low and intermediate HeartScore risk group from 78 to 147 participants (from 2.7 % to 4.2 %, p = 0.001), when estimating the risk based on the algorithm for low-risk countries. As a consequence of the reclassification of Germany and Denmark as low-risk countries more people with severe atherosclerosis will be classified as having a low or intermediate risk of fatal cardiovascular disease. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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Population-wide dietary sodium reduction is considered a priority intervention to address sodium-related chronic diseases. In 2010, the Canadian government adopted a sodium reduction strategy to lower sodium intakes of Canadians; however, there has been a lack of coordinated action in its implementation. Our objective was to evaluate Canadians' concern, actions, reported barriers, and support for government-led policy interventions aimed at lowering sodium intakes. We conducted a survey among Canadians about sodium knowledge, attitudes, and behaviours. Data were weighted to reflect the 2006 Canadian census. Among 2603 respondents, 67.0% were concerned about dietary sodium and 59.3% were currently taking action to limit sodium intake. Those aged 50-59 years (odds ratio [OR], 1.79; 95% confidence interval [CI], 1.17-2.72) and 60-69 years (OR, 1.63; 95% CI, 1.05-2.55) were more likely to be concerned about sodium vs younger individuals (20-29 years), as were hypertensive patients vs normotensive patients (OR, 4.13; 95% CI, 3.05-5.59). Older age groups and those with hypertension (OR, 3.48; 95% CI, 2.58-4.69) were also more likely to limit sodium consumption. Common barriers to sodium reduction were limited variety of lower sodium processed (55.5%) and restaurant (65.8%) foods. High support for government-led actions was observed, including interventions for lowering sodium levels in processed (86.6%) and restaurant (72.7%-74.3%) foods, and in food served in public institutions (81.8%-82.3%), and also for public education (80.4%-83.1%). There was much less support for financial incentives and disincentives. In conclusion, these concerns, barriers, and high level of support for government action provide further rationale for multi-sectoral interventions to assist Canadians in lowering their sodium intakes.
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An abstract is not available.