ARTIFICIAL NEURAL NETWORK APPROACH
FOR OBESITY-HYPERTENSION CLASSIFICATION
Octavian Postolache
1
, Joaquim Mendes
2
1
Instituto de Telecomunicações, Av. Rovisco Pais, 1049-001, Lisboa, Portugal
2
IDMEC, Faculdade de Engenharia UP, Portugal
Gabriela Postolache
3
, Pedro Silva Girão
4
3
Universidade Atlantica, Escola de Saude, Oeiras, Portugal
4
Instituto de Telecomunicações, Av. Rovisco Pais, 1049-001, Lisboa, Portugal
Keywords: Obesity-hypertension syndrome, Artificial neural network.
Abstract: One of the newest targets of public health is management of obesity-hypertension. In this paper is presented
the use of an artificial neural network based model for objective classification of obesity-hypertension.
Different neural network architectures as part of hybrid processing scheme including comparators and
competitive processing blocks were developed and tested. The neural network functionality is the
classification of the individuals according to the obesity risks. The results show that the neural network
classifier is consistent with the standard criteria suggested by the obesity and hypertension guidelines.
1 INTRODUCTION
Obesity is rapidly turning into an “epidemic”
afflicting much of the industrialized world. Obesity
is a major risk factor for serious non-communicable
diseases such as cardiovascular disease,
hypertension, stroke, diabetes mellitus and various
forms of cancer. Therefore, it will be one of the
major causes of death, according to the estimation of
the World Health Organization, which suggest that
by 2025 approximately 60% of deaths worldwide
will be caused by circulatory diseases and cancers
(WHO, 2000). The relationship between obesity and
hypertension appears to be non-linear and exists
throughout the non-obese range. Obesity by itself
possibly accounts for 78% and 65% of essential
hypertension in men and women, respectively,
according to data from the Framingham Cohort
(Kannel et al., 1993). Hyperinsulinemia,
hyperleptinemia, hypercortisolemia, renal
dysfunction, altered vascular structure and function,
enhanced sympathetic and renin/angiotensin system
activity, and blunted natriuretic peptide activity
stand out as major contributory mechanisms to
“obesity - hypertension” (Tuck et al., 1981; Hall et
al., 2002; Mansuo et al., 2000; Engeli & Sharma,
2002). Furthermore, according to the European
Society of Hypertension and the European Society
of Cardiology (ESH-ESC) guidelines, hypertension
induces high added risk for target organ damage,
diabetes, or associated clinical conditions (ESH-
ESC, 2003). Moreover, organ damage and
associated clinical condition in obese people
increase with the extent of risk factor clustering
(Narkiewicz, 2006a). Therefore, objective diagnosis
of obesity-hypertension
is an important public health
challenge because of its high frequency and
concomitant risk of cardiovascular and kidney diseases.
Details regarding hypertension risk stratification
have been published: 1999 WHO/ISH Guidelines
(Chalmers et al., 1999), 2003 ESH-ESC Guidelines
(ESH-ESC, 2003), JNC6 of USA (Sheps et al.,
1997) and the Guidelines for the Management of
hypertension of China (Ministry of Health People’s
Republic of China, 1999). In what concerns obesity,
the evidences from the literature show a continuous
relationship between gradation of body mass index
(BMI), waist circumference, waist to hip ratio and
514
Postolache O., Mendes J., Postolache G. and Silva Girão P. (2009).
ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION.
In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 514-520
DOI: 10.5220/0001553705140520
Copyright
c
SciTePress
health risk (WHO, 2000; Sowers et al., 2001; Health
Canada, 2003; Lau et al., 2007, Ergun, 2008).
Nowadays, diagnosis of obesity is made mainly
according to body mass index (BMI) and waist
circumference. Unfortunately, comparisons by
ethnicity and sex have revealed that the universal
application of criteria for obesity and central obesity
developed in Caucasians leads to an overestimation
of risk in African Americans and an underestimation
of risk in South Asians (Sumner et al., 2007). Also,
obesity identification by generational trends showed
those generations prior to the "Baby Boomer", who
were not exposed to more recent unhealthy food
consumption patterns as younger people, are less
likely to be obese, in spite of their age (Garavagli &
Synthelabo, 2004). In addition, geography also plays
a role (Garavagli & Synthelabo, 2004). Also, new
Canadian Guidelines on the management and
prevention of obesity in adults and children
emphasised the importance to measure depression
and other mood disorders beyond BMI, waist
circumference and laboratory parameters (as fasting
blood glucose level, total cholesterol, LDL
cholesterol, HDL cholesterol, triglycerides, ratio of
total cholesterol to HDL cholesterol, liver enzyme
levels and urine analysis) (Lau et al., 2007).
Moreover, reverse epidemiology it was shown in
patients characterized by undergoing haemodialysis,
with increased survival of obese patients
(Narkiewicz, 2006b, Salahudeen et al., 2006). To
deal with these issues, much research is needed to
develop improved statistical methods that permit
coherent management, taking into account the global
risk of a patient with obesity-hypertension rather
than to focus solely on biometric variables and blood
pressure values. Objective diagnosis of obesity-
hypertension is important not only to prevent the
progression of obesity and hypertension signalling
the health risk but also for treatment management of
hypertension in obese people (Dentali et al., 2007;
Messerli & Schmieder, 1986, Narkiewicz, 2006a,
Narkiewicz, 2006b) reduction of the anaesthesia-
related mortality (Saravanakumar et al., 2006), and
health costs reduction (WHO, 2008; Lewis & Man,
1999).
The aim of this study now reported was to
evaluate the performance of an artificial neural
network (ANN) for modeling and objective
identification of the obesity-hypertension
physiopathology joining different informations
related to the patient health status. The ANN, a kind
of black box model, shows certain advantages over
other methods for multivariate modeling. The major
advantage used in the present application is that with
sufficient data, an ANN can be trained to learn the
relationship between the inputs (clinical examination
data) and outputs (obesity-hypertension classes)
even if the mechanism of the relationship is
unknown or unclear, as in the present case where
few models are described in literature for
hypertension-obesity stratifications criterion (see
Aneja et al., 2004, Narkiewicz, 2006a, Narkiewicz,
2006b). The ANN ensures great flexibility
associated with computer diagnosis of the
hypertension-obesity sindrome (Ning et al., 2006;
Bidiwala et al., 2004; Lapuerta et al., 1995;
Mangiameli et al., 2004; Orunescu et al., 2004, Poli
et al., 1991). Our application of the ANN permits
better diagnosis and management of the obesity-
hypertension syndrome.
2 METHODS
The problem in diagnosis and management of
obesity is that the relationship between different
items (e.g. laboratory results and/or symptoms) is
not always well established and that there exists a
myriad of exceptions for every rule. A learning
process expert system could be developed using
neural networks for medical decision aid. The
schematic drawing of the model is shown in Fig. 1.
The figure includes a multilayer perceptron neuronal
network classifier (MLP-NN) whose inputs are
expressed by values of clinical examination data.
MLP-NN classifier
H
O
IN
COMPETE proc.
C
& &
0
1
P
1
OH
1
OH
m
ohc
1
ohc
m
P
n
ohca
1
ohca
1
Figure 1: Obesity-Hypertension Neural Network
Processing Scheme (ohcm – obesity-hypertension classes,
ohca1, ohca2- obesity-hypertension additional classes).
ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION
515
The competitive processing block calculates the
binary output (ohc
1
, ohc
2
…) using the values
obtained with the MLP-NN classifier. Additional
classes (ohca
1
, ohca
2
) are obtained by using the level
of blood-pressure and the hypertension thresholds
(e.g. SBP > 140mmHg, DBP > 90mmHg) and the
results of competitive processing block for the
particular cases of normal and overweight
individuals.
Table 1: Variables range used to describe obesity-
hypertension syndrome class 3.
A brief description of the main blocks of the
obesity-hypertension general classifier is presented
next.
2.1 The Input of the Model
In the present study, a model of the clinical
parameters distribution that is essential for diagnosis
and monitoring was built using published data. The
evidences on relation between hypertension and
obesity are mainly documented on adult people (18-
50 years), overweight, or with class 3 obesity
(BMI>40 kg/m
2
). However, there are a lack of
studies that may give a thorough view on the main
clinical indicators (see Aneja et al., 2004), which
may better describe the present state of an individual
and possible future evolution of hypertension in
relation with obesity.
Since there were no well-characterized real
datasets available that fit with all obesity classes
described in our study, a simulation study was
proceed. The input data considered was essential
clinical information for obesity-hypertension
association, being expressed by the values of the
following parameters: body mass index (BMI), waist
girth, blood pressure, heart rate, triglyceride,
glucose, high-density lipase (HDL)-cholesterol
(Aneja et al., 2004), total cholesterol (Ai et al., 2000,
Aguilera et al. 2008), low-density lipase (LDL)-
cholesterol (Aguilera et al., 2008, Gupta et al.,
2007). The simulated values for different obesity
classes were adjusted according to the published
data applying uniformly random data distribution for
specific data intervals. The maximum values for
morbid obese group were built taking into account
clinical cases of obesity described in 19
th
century –
Daniel Lambert (Table 1). The samples sizes were
simulated for 400 to 1600 individuals, 40 to 200
individuals for each class.
The simulated data were not defined using sex-
specific observation points. However, a future study
taking into account the data distribution versus sex
and age will be considered.
Although the parameter settings are not
exhaustive in terms of all physiopathological
plausible situations, the outlined conditions are
reasonable, mainly designed to differentiate obesity
and hypertension features.
2.2 The Output of the Model
The classification of the simulated data were made
according to 2000 WHO (WHO, 2000b), 2003
Health Canada (Health Canada, 2003) and 2003
ESH-ESC Guidelines (ESH, 2003). Presently,
diagnosis of obesity is made mainly according to
BMI index:
2
= mhBMI
(1)
where m-personal weight and h-personal height.
The average BMI index in normal people is
between 18.5 and 24.9 kg/m
2
. Obesity is defined as a
BMI>30 kg/m
2
; morbid obesity is when BMI>35
kg/m
2
. There is a continuous relationship between
gradation of BMI and health risk and between waist
circumference and health risk. Hypertension is
considered when systolic blood pressure (SBP) is
>140 mmHg and diastolic blood pressure (DBP) is
>90 mmHg. Current guidelines suggest that essential
laboratory investigation for hypertension diagnosis
should include: blood chemistry for fasting glucose,
total cholesterol, HDL-cholesterol, LDL-cholesterol,
triglycerides, urate, creatinine, sodium, potassium,
haemoglobin and haematocrit, decreased creatine
clearance, liver enzyme, the detection of an elevated
urinary excretion of albumin, an electrocardiogram
or echocardiography (Narkiewicz, 2006a, Lau et al.,
2007). For the sake of application in practice, the
input and output of the model was simplified. The
output of the model was defined as: healthy subject
(N), hypertensive with BMI normal (H), overweight
(OW), overweight with hypertension (OWH),
Variables OHC
3
Normal
Body mass index (kg/m
2
) 40-85 18.5-24.9
Waist girth
a
(cm) 150-170 58-88
Waist-to-hip ratio (cm/cm) 1.4-1.6 0.6-0.9
Systolic blood pressure (mmHg) 140-190 90-125
Diastolic blood pressure (mmHg) 90-120 55-84
Heart rate (bpm) 90-140 55-95
Serum triglycerides (mg/dL) 200-350 30-175
Total cholesterol
(µmol/L) 200-350 250-680
Serum HDL cholesterol (mg/dL) 35-50 50-60
Serum LDL cholesterol (mg/dL) 30-40 50-130
Glucose (mg/dL) 90-220 70-110
BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing
516
obesity class I (OC
1
), obesity class I-hypertension
(OHC
1
), obesity class II (OC
2
), obesity class II-
hypertension (OHC
2
), obesity class III (OC
3
), and
obesity class III-hypertension (OHC
3
).
2.3 Model Architecture and Training
The type of the neural network used in this work
was multilayer perceptron (MLP-NN). The network
training is based on supervised learning techniques
that were implemented and tested for a better
classification of the assessed persons. Thus, training
algorithms such as Levenberg Marquardt back
propagation (LMBP) and Generalized Delta Rule
were used.
The MLP-NN classifier architecture includes a
set of three layers. The input layer receives a set of
normalized values associated with obesity-
hypertension clinical examination data that are
delivered by the pre-processing block. The number
of input nodes was included in the 10 to 19 interval.
The hidden layer learns to encode these quantities
and includes sigmoidal neurons (logsigmoid,
tansignoid). During the MLP-NN design a practical
approach concerning the number of hidden neurons
for a short training time (t
train
) and good
classification of the individual in the obesity-
hypertension classes OHclass was performed.
The output layer produces the desired
classification results and is expressed by a number
of linear neurons n
out
(8 to 10 neurons). The values
associated with the output neurons are included in
the [0, 1]. Thus a value near one underlined that an
OHclass was identified, while ‘0’ corresponds to no
OHclass identification. The description equation of
neuronal network classifier is:
()(
outhiddenhiddenhiddenoutout
BBXWfWfY ++=
)
()
(2)
where X is the input and Y is the output, W
hidden
and
W
out
are the weights of the hidden and output layer
neurons, B
hidden
and B
out
are their biases, f
hidden
is a
sigmoid function for the hidden layer neurons
(logsigmoid and/or tansigmoid in the present
application) and f
out
is a linear function for the output
layer neurons.
Referring to the MLP-NN
classifier
design, different
training algorithms were applied for shorter training
times and accurate classification. Thus, fast
backpropagation algorithms expressed by gradient
descent algorithm with momentum and variable
learning rate, or Levenberg-Marquardt back
propagation algorithm were employed to update the
weights and biases of the net in the training process.
The training process requires a set of samples
expressed, in the present case, by physiopathological
variables associated with the individuals under
obesity-hypertension and corresponding known
OHclass. During training, the weights and biases in
the model are adaptively refined to ensure a relative
optimization of the network performance related to
the classification capability. As performance
measurement functions the sum-square error (SSE)
and mean-squared-error (MSE) were used:
=
==
N
i
ii
N
SSE
MSEnnohohSSE
1
2
(3)
where N is the number of input samples,
oh
i
is the
target output imposed for known individual data and
nnoh
i
is the network output for given weights and
biases. The
nnoh
i
values in the [0, 1] interval. In
order to conclude about the obtained OHclass for a
given individual, a competitive processing block
(based on
compet() MATLAB function) transforms
the real values obtained at the MLP-NN outputs in
Boolean values corresponding to one of the
OHclasses.
After weights and biases calculation using the
above mentioned algorithms, a testing set was
employed to validate the neural network
classification capabilities using simulated and real
values.
3 RESULTS
The model of obesity-hypertension classes has been
tested using simulated data and experimental data
from a group of 30 voluntary persons. The main
characteristics of the persons included in the study
are presented in Table 2. The studied group includes
3 subjects with normal BMI, 15 persons with normal
BMI and hypertension, 3 overweight persons, 4
overweight with hypertension, 1 person with obesity
class I, 1 person with obesity class II, 2 hypertensive
subjects with obesity class II and 1 hypertensive
subject with obesity class III.
A set of data corresponding to different obesity-
hypertension classes, normally and overweight
individuals, and the corresponding physio-
pathological parameters were used to train the neural
processing scheme associated to obesity –
hypertension model.
ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION
517
Table 2: The main characteristics of the patients included
in the study.
Median Average
Age (y) 58.50 (33-87) 60.37
BMI (kg/m
2
) 24.37 (18.57-46.88) 25.21
Waist girth (cm) 101.00 (67-131) 100.90
SBP (mmHg) 158.10 (112-189) 161.00
DBP (mmHg) 81.50 (56-101) 78.90
Heart rate (bpm) 75 (60-99) 76.67
Triglyceride (mg/dL) 171 (60-300) 163.37
Total cholesterol
(μmol/L)
178 (83-264) 174.27
HDL cholesterol (mg/dL) 44 (21-100) 46.07
Glucose (mg/dL) 84 (68-325) 106.43
The MLP-NN training set was expressed by a
10×400 input matrix and a 10×400 target matrix
while the testing set was expressed by a matrix with
the same dimensions as the training matrix. Both,
training and testing data sets were obtained by
simulation according with the values obtained in
clinical trials. Additionally, a reduced testing set for
clinical trial data expressed by 10×30 input matrix
and 8×30 output matrix was used to test the designed
processing architecture.
Different neural network architectures were
designed and tested. Considering the problem
complexity and the amount of data used for training
and testing, the MLP-NN with hidden layer
characterized by 5 to 15 neurons was employed.
Good results were obtained for n
hidden
=10 logsigmoid
neurons. The associated error curve, during the
training with SSE=0.2 training stop condition, is
shown in Fig. 2.
0 20 40 60 80 100 120 140 160 180
10
-1
10
0
10
1
10
2
10
3
10
4
Epoch
Sum-Squared Error
Sum-Squared Network Error for 192 Epochs
Figure 2: The error curve of the MLP-NN during the
training phase (10 logsignoid neurons and SSE=0.2
training stop condition).
The weights and biases values obtained during
the training are used to perform the normal (N),
overweight (OW) and obesity-hypertension (OH)
classification of the individuals using the simulated
testing data available for all of eight hypertension
and obesity classes. Real testing data was available
only for reduced number of classes (N, OW, OC
1
,
OHC
2
and OHC
3
). Several results concerning the
classification scheme performance are presented in
Table 3 that shows good classification accuracy for
ANN training and testing data sets obtained by
simulation. Thus, considering testing data obtained
by simulation, the output of the neural network
classification scheme is expressed by the
classification histogram (Fig. 3).
Table 3: Classification results for MLP-NN classifier
characterized by 10 logsigmoid hidden neurons.
Class
Total Classification Accuracy
Training Testing
N
97.5% 95%
OW
100 % 97.5%
OC
1
100% 95%
OHC
1
97.5% 72.5%
OC
2
97.5% 97.5%
OHC
2
100% 82.5%
OC
3
97.5% 82.5%
OHC
3
100% 100%
60
1 2 3 4 5 6 7 8
0
10
20
30
40
50
classes
Nt
Figure 3: N-OW-OH classification histogram (Nt –
number of occurrences).
The accuracy of the ANN classifier for real
clinical data associated with the diagnosis of the 30
volunteers was included in the study and the results
are represented in Table 4. As can be observed in the
table, the used real data include several of the
considered classes caused by limited number of the
data provided by the Hypertension Hospital Service.
BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing
518
Table 4: Accuracy of the ANN model for classification of
individuals from experimental group (30 individuals).
Class
N OW OC
2
OHC
2
OHC
3
Accuracy
27% 57.14% 100% 50% 0%
Considering that the experimental group includes
individuals associated with N and OW classes and
less in the obesity classes (OC1, OHC2, OHC3) the
very low or very high classification success in
several classes is expected. Better results are
expected to be obtained when an extended
experimental data for each OH classes will be used
for the designed ANN classifier.
4 CONCLUSIONS
There is a lot of knowledge on obesity, but
thoroughly view of the phenomenon remains to be
done. The model based on ANN with extended
clinical examination data represents an important
method for classification of individuals with obesity-
hypertension syndrome. A hybrid processing based
on backpropagation neural network and competitive
processing blocks was developed. Results for
simulated and experimental data recommend the
implemented processing scheme as a good classifier
and decision support tool.
Future work will be dedicated to the increase of
the classification accuracy by optimizing the neural
network architecture. Additionally, according to the
cooperation of the Hypertension Hospital unit, real
data for different subjects at different times will be
used to extract important information on
cardiovascular risk level associated with each
obesity-hypertension classe.
ACKNOWLEDGEMENTS
The authors wish to thank Drª. Monica Ferreira
(Hospital Santa Maria of Lisbon) for the support to
the research activity. The research was funded by the
Portuguese Research Foundation - FCT through
PTDC/EEA-ACR/75454/2006 research project.
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