A Risk Factors Screening Method in the Context-aware System of

Hypertension

Duoyi Xie

1,2

, Guixia Kang

1,2

and Longfeng Chen

1,2

Key Laboratory of Universal Wireless Communications, Ministry of Education,

Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Beijing, China

Wuxi BUPT Sensory Technology and Industry Institute CO.LTD, Wuxi, China

Keywords: Hypertension, Context-aware System, Feature Selection.

Abstract: Hypertension has become a health problem that seriously endangers human life and is the leading cause of

cardiovascular disease. Many patients do not know exactly whether their blood pressure is well controlled

or not, which makes their conditions worse. A context-aware intelligent system can help patients to analyse

their control situation of blood pressure (BP) and provide feedback. It is especially important to determine

whether the risk-factors input in the context-aware system of hypertension is appropriate. The choice of risk

factors will affect the classification performance and accuracy of the system. The risk factors screening

method for hypertension proposed in this paper combined the random forest algorithm and stability

selection (RFSS). It can remove the redundant context information, and leave the key factors of BP control

situation. Experimental results showed that the prediction accuracy achieved more than 77% prediction

accuracy, and dimension of risk factors reduced by 59%. The results indicated that RFSS is an effective

method in the screening of risk factors and the prediction of hypertension.

1 INTRODUCTION

Hypertension is a leading cause of death and

disability-adjusted life-years worldwide (Lim et al.,

2012). There are 270 million hypertensive patients

in China. Annual patient increases by more than 10

million. More than 1 million people die from high

blood pressure every year in China. Three-quarters

of the survived patients were disabled due to

hypertension. However, the awareness rate and

control rate of hypertensive population are only 36%

and 28%, respectively (Chen et al., 2014). Blood

pressure of 120/80 mm Hg or higher is linearly

related to risk for fatal and nonfatal stroke, ischemic

heart disease, and noncardiac vascular disease, and

each increase of 20/10 mm Hg doubles the risk for a

fatal cardiovascular disease event (Carey and

Whelton, 2012). Evidence shows that hypertension

can be prevented and controlled through monitoring

and treatment (Whitworth and Chalmers, 2004).

Wireless eHealth (WeHealth) has developed

rapidly in China in recent years, and remote

hypertension monitoring is one of its most important

applications (Kang and Zhang, 2010). The

hypertension monitoring system can increase the

awareness rate and control rate of hypertensive

population by feeding back their situation of blood

pressure (Sandi et al., 2013). The management of

hypertensive patients is mainly based on the blood

pressure levels. However, the threshold of

hypertension will be different due to the differences

in patient function. Decisions on the management of

hypertensive patients should not only take blood

pressure levels into account, but also the other

cardiovascular risk factors (Whitworth and Chalmers,

2004). Therefore, in addition to blood pressure,

comprehensive analysis of other risk factors in the

context information can help people better recognize

their blood pressure condition. The formation of

hypertension is related to a variety of factors,

including age, lifestyle, family history, etc. (Hong-

Tao et al., 2007; Kunes and Zicha, 2009). The

context information obtained by the monitoring

system contains risk factors and redundant

information that affects the classification accuracy.

Therefore, we need to screen the risk factors of

hypertension in the context information to improve

the classification effect of the monitoring system. In

this way, the patients who take medicine can be

properly guided to continue medication to prevent

Xie, D., Kang, G. and Chen, L.

A Risk Factors Screening Method in the Context-aware System of Hypertension.

DOI: 10.5220/0007672500490056

In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security (IoTBDS 2019), pages 49-56

ISBN: 978-989-758-369-8

the deterioration of the disease. At the same time,

the study can assist doctors to diagnose patients'

blood pressure condition individually.

In previous studies, feature selection algorithms

were often used to screen for risk factors. Feature

selection methods are divided into selection

mechanisms: Filter, Wrapper, and Embedded. Filter

relies on the data samples themselves, with a low

time complexity. Ding and Peng (2005) proposed

mRMR (minimum-redundancy maximum-

relevancy) feature selection method. The algorithm

considers not only the correlation of each feature

attribute with respect to the class label, but also the

redundancy of the internal relationship of the feature

attributes set. Wrapper algorithms select features by

using learning algorithm results, with a better result

and a higher time complexity (Kohavi and John,

1997). Hu and Bao (2015) proposed a wrapper

feature selection algorithm for short-term load

forecasting (STLF) data, which has excellent

selection effect. The embedded algorithm takes

feature attribute selection as part of the training

process and has balanced efficiency and accuracy. In

recent years, it has become a research hotspot.

Koenigstein and Paqued (2013) proposed an

embedded algorithm based on Bayes matrix

factorization. The classification model can

automatically identify and use informational

features, and useless features can be subtracted.

For a single method, the screening rate and

accuracy are often not balanced. In this paper, we

propose a context-aware system to assess the

treatment effect in the intelligent monitoring of

hypertensive patients. For the decision making

module of the system, we propose a filter-embedded

feature selection method to screen risk factors of

hypertension. By using the support vector machine

(SVM) classification algorithm to verify the

screening effect, this method can improve the

screening rate and ensure the accuracy of this

system. The entire system therefore improves

performance, which can service hypertensive

patients preferably.

2 SYSTEM ARCHITECTURE

To make a comprehensive evaluation of

hypertensive patients’ treatment effect, blood

pressure data should not be the only reference. The

proposed monitoring system is context-aware and

takes into account other risk factors. Risk factors

screening method and data mining techniques are

utilized in classifying the treatment effect. The

overall architecture is shown in Figure 1. It consists

of three modules: data acquisition (DA), decision

making (DM), and diagnostic feedback (DF).

Figure 1: The proposed system architecture.

2.1 Data Acquisition Module

DA module gathers and stores patients’ medical

information. The way to obtain the context is

divided into measurement data acquisition, text data

acquisition and sample data acquisition according to

different acquisition paths and uses. Measurement

data is collected by hardware sensors. Text data is

obtained by textual input. Sample data is stored in

medical databases.

2.2 Decision Making Module

This is the core module of the entire system. It

determines the level of patient treatment

effectiveness. In the intelligent monitoring of

hypertension, it is necessary to evaluate the level of

blood pressure control status of hypertensive

patients, and make corresponding diagnostic

feedback according to the level. Data drawn from

the DA module must be preprocessed first. The data

preprocessing process is the core step and the focus

of this paper. It includes data cleaning, analysis,

feature selection, and evaluation. The specific

method will be detailed in Section 3. Then the

preprocessed data is put into the data mining

algorithms for training and classification to judge the

condition. By analyzing the patient's blood pressure,

age, weight and other contextual information, the

patients are divided into two groups by professional

doctors: Good Treatment (GT) and Poor Treatment

(PT). Finally the hypertensive patients are classified

into these two groups by the system based on their

risk factors. The accuracy of the DA module is

verified by classification results.

IoTBDS 2019 - 4th International Conference on Internet of Things, Big Data and Security

2.3 Diagnostic Feedback Module

DF module is a small inference system to generate

the feedback according to DM output and patient’s

relevant information. Knowledge Base contains all

the knowledge needed for the proper treatment of

hypertensive patients, varying from drug therapy to

non-drug therapy (mainly lifestyle modifications).

Rule Base is a set of rules regarding medical

diagnosis. The inference engine gives an automatic

feedback to the input treatment effect based on the

Knowledge Base and Rule Base. The feedback is

then returned to the patient, either by WeChat,

Email, or on the web.

3 SCREENING METHODS

The DM module extracts the hypertension risk

factors from the context information acquired from

the DA module and inputs them into the classifier to

obtain the patient's hypertension control condition.

There is a large amount of multi-dimensional

redundant information in the context information.

Redundant information will decrease the speed of

the system and interferes with normal decision

making. Therefore, it is particularly important to

filter risk factors from multi-dimensional context

information correctly and effectively. Accurate

extraction of features can increase the accuracy of

the classifier and reduce data dimensions. After each

step of screening, we use the same classification

algorithm to evaluate the screening results to test the

effect of the methods.

3.1 Data Preprocessing

This study screened 8619 people who met the

criteria as the data set from Beijing We-Health

Platform. The data included carcinoembryonic

antigen (CEA), sex, age, height, weight, white blood

cells, red blood cells, haemoglobin (HGB), red

blood cell specific volume (HCT), erythrocyte mean

corpuscular volume (MCV), mean corpuscular

haemoglobin (MCH), mean corpuscular

haemoglobin concentration (MCHC), red cell

distribution width coefficient variation (RDWCV),

red cell distribution width standard deviation

(RDWSD), platelet count (PLT), mean platelet

volume (MPV), platelet distribution width (PDW),

monocyte (MON), MON%, Granulocytes (GRA),

GRA%, lymphocyte (LYM), LYM%, alanine

aminotransferase (ALT), UREA, serum creatinine

(Cr), uric acid (UA), serum total cholesterol (TC),

triglyceride (TG), fasting plasma glucose (FPG),

specific gravity (SG), PH, a total of 32 dimensions.

Body mass index (BMI) can accurately reflect the

combined indicators of height and weight, so we

combined these two features into BMI. These data

were labelled to indicate the type, GT and PT.

According to statistics, there were about 47% of GT

data and 53% of PT data. The data contained

diastolic pressure and systolic pressure, but we did

not consider them to ensure the objectivity of the

risk factor screening process. We randomly divided

the data set into 70% training set and 30% test set. In

the following steps, the models were trained by 10-

fold cross validation. A total of 2586 data were used

for the tests. To guarantee the same ratio of the two

types of data, there were 1372 cases of GT data and

1214 cases of PT data.

3.2 Filter by PCC

The continuous variable correlation measure based

on Pearson’s correlation coefficient (PCC)

significance test can quickly test whether there is

linear correlation between data. The source of

context information used in this paper was the

physical examination information. The evaluation

index was mostly linear, so we used the PCC for

preliminary screening. For the given sample points,

we can calculate the PCC. This method can screen

out features that have little correlation with blood

pressure.

The calculation formula is as follows:

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3.3 Random Forest Screening

Random forest algorithm has many advantages,

including high accuracy, robustness and ease of use,

making it one of the most popular machine learning

algorithms (Madan et al., 2008). It randomly selects

 samples from the sample set and  attributes from

all attributes. After that, it selects the best

segmentation attribute node to establish the CART

decision tree. The above steps are repeated m times

to establish m decision trees and obtain m classifiers

(Breiman, 2001).

However, this kind of impurity-based screening

method has a bias. Once a feature is selected, the

importance of other features will drop sharply

because the impurity has been lowered by the

selected feature. So other features are difficult to

reduce so much impurity. Therefore, the feature

A Risk Factors Screening Method in the Context-aware System of Hypertension

selected at first gets the high score while the score of

other related features gets low score, which is easy

to cause misunderstanding.

3.4 Random-forest Stability Selection

Stability selection is a method based on a

combination of subsampling and selection

algorithms. This method provides finite sample

control for some error rates of false discoveries and

hence a transparent principle to choose a proper

amount of regularization for structure estimation

(Nicolai and Peter, 2010).

We improved the screening method for the

instability of feature scores in random forest

algorithms. Firstly, the filter method was used. Then

we combined the random forest algorithm and the

subsampling to an improved screening method:

random-forest stability selection (RFSS). Its main

idea is to run random forest algorithm on different

data subsets and feature subsets, repeat constantly,

and finally aggregate feature selection results. We

used the frequency (the number of times selected as

an important feature divided by the number of times

its subset was tested) at which a feature was

considered to be an important feature as an

evaluation indicator. Ideally, important features

would score close to 100%. A slightly weaker

feature score would be a non-zero number, while the

most useless feature score would be close to zero.

3.5 Evaluation Algorithm Selection

For different screening methods, we used a unified

classification algorithm to verify the screening

effect. The support vector machine (SVM)

(Cristianini and Shawe-Taylor, 2000) has been

introduced as an efficient technique for solving

various function estimation problems, especially for

the pattern classification problems (Vapnik et al.,

1997). SVM is among the most robust and accurate

methods in all well-known data mining algorithms.

The final decision function of the SVM is

determined by only a few support vectors, and the

computational complexity depends on the number of

support vectors. SVM can verify whether we have

selected the most appropriate risk factors and avoids

the "curse of dimensionality". Therefore, we used

SVM to verify the screening effect.

4 EXPERIMENTAL RESULTS

We verified the accuracy of each screening method

by SVM algorithm, and judged the screening effect

of risk factors through multiple evaluation indicators

including sensitivity, specificity, accuracy and

dimension.

4.1 Performance Analysis

Table 1 shows the classification result of raw data

without using a screening method through 10-fold

cross-validation. 650 of the 1214 PT patients were

predicted to be accurate and the sensitivity is 54%.

The accuracy of this model is 66%. The risk factor

dimension is 32.

Table 1: Classification result without using a screening

method.

Actual

Total

Predicted

1068

564

304

650

Total

1372

1214

2586

Then we used the PCCs to filter features. We can

see the result in figure 2. We sorted 32 features

based on scores by PCCs. The feature with 0 value

indicates that the probability of a two-tailed test is

greater than 0.05, indicating that they cannot be used

as a risk factor. Meanwhile, the correlation values of

PLT, LYM, LYM%, MCV, MPV, and MON for

blood pressure were less than 0.05. This means that

these parameters, relative to other parameters, do not

directly reflect or affect a patient's blood pressure

condition. So we screened these parameters out

because they were basically not helpful for the

classification models.

Table 2 shows the result using the filter

screening method through 10-fold cross-validation.

The feature dimension reduced from 32 to 23, and

the data indicated that the sensitivity and accuracy of

the classification had increased significantly. 896

patients were predicted to be accurate and the

sensitivity is 73%. The accuracy of this model raised

to 75%. Dimension was 28% lower than before.

However, the number of risk factors was still too

large. Therefore, we used random forest screening

methods to reduce feature dimension and further

determine the risk factors on this basis.

Table 2: Classification result with using PCC.

Actual

Total

Predicted

1073

318

299

896

Total

1372

1214

2586

IoTBDS 2019 - 4th International Conference on Internet of Things, Big Data and Security

Figure 2: The scores by using PCC.

Figure 3 shows the scores of the features after

using the random forest method. As mentioned in

Section 3, once a feature was identified in the

random forest screening process, the scores of other

associated features may drop suddenly, leading to

filter out some useful features. If we chose features

with high score by the result, the sensitivity and

accuracy would decrease in the classification

process.

Figure 3: The scores by using random forest method.

Table 3 shows the classification result using random

forest method after filter method with 10-fold cross-

validation. The risk factors were further reduced to

13. The dimension was 59% lower than the original.

But only 742 PT patients were predicted to be

accurate. The sensitivity and the accuracy of this

model decreased to 62% and 71%.

Table 3: Classification result with using random forest.

Actual

Total

Predicted

1085

472

287

742

Total

1372

1214

2586

In order to solve this problem, we chose RFSS

method to calculate the key degree of each factor

with more objective scores. Figure 4 shows the

scores result by using the RFSS method.

Figure 4: The scores result by using the RFSS method.

The highest four features’ scores were 1.0, which

means that they were selected as useful features (the

score was affected by the regularization parameter

alpha) every time. The next few features’ scores

began to decline, but the decline was not particularly

sharp like the result by using the random forest

method. It can be seen that RFSS method was

helpful in overcoming overfitting and

misunderstanding data. Good features did not have

low scores due to feature correlation. So the method

is better than the method only using random forest.

Table 4 shows the classification effect by RFSS

method through 10-fold cross-validation. The risk

factor dimension was still 13, but at the same time,

we improved the classification sensitivity and

accuracy. 909 of the 1214 PT patients were

predicted to be accurate and the sensitivity raised to

75%. The accuracy of this model was 77%. This

method achieved the desired effect, that is, less

dimension and higher accuracy.

0,1

0,2

0,3

0,4

0,5

age

BMI

sex

FPG

HCT

HGB

RBC

UREA

ALT

CEA

WBC

GRA

GRA_

MCH

MON_

MCHC

PLT

LYM_

_LYM

MCV

MPV

MON

RDWSD

RDWCV

PDW

systolic pressure diastolic pressure

0,2

0,4

0,6

0,8

1,2

age

sex

BMI

HCT

FPG

RBC

WBC

UREA

HGB

CEA

MON_

ALT

GRA

MCH

GRA_

A Risk Factors Screening Method in the Context-aware System of Hypertension

Table 4: Classification result with using RFSS.

Actual

Total

Predicted

1082

305

290

909

Total

1372

1214

2586

4.2 Discussions

Finally, we selected 13 features to be risk factors for

our proposed context-aware system through the

RFSS method. They are age, sex, TC, TG, BMI,

HCT, FPG, RBC, WBC, UA, UREA, Cr and HGB.

The accuracy of the results can be supported by the

classification results and related literature.

In the last few years, there had been many

studies on rick factors of hypertension. The results

showed that age, sex (Virdis et al., 2002), BMI,

genetic factors, waist circumference (Ashwell et al.,

2012), TG (Wang, 2013), Urea (Pearson et al.,

2001), were the most important risk factors affecting

hypertension. This is consistent with the result of

selecting important features by using a random

forest algorithm.

Table 5 shows the comparison of classification

effect by selecting different features as risk factors

according to different screening methods. All

methods had been tested by 10-fold cross validation.

We can see that the sensitivity was low when we

used all the features for classification, and the high

dimension would affect the system performance.

After the filter method, the indicators had improved,

but the number of dimension still cannot make us

satisfied. Therefore, in the method III and method

IV, we adopted machine learning method, and the

dimension was all controlled to the same number 13

to facilitate the comparison of experimental results.

Compared with method I and III, the accuracy and

sensitivity of method IV were greatly improved.

Compared with method II, there was an advantage in

the number of dimensions.

In the study of Maryam Tayefi et al., (2017),

they used decision tree algorithm to study

hypertension related factors, and the accuracy can

reach 73%. Besides that, in the study of Wang et al.,

(2014), they used a logistic regression and artificial

neural network-based approach to predict

hypertension. The sensitivity and accuracy can reach

49% and 77%. For comparison, we used the same

methods to train the data set of this study through

10-fold cross-validation. As shown in Table 6, The

result for testing data set shows that the accuracy,

sensitivity and specificity of using decision tree

training model was 73%, 61%, 78%. And the neural

network algorithm could reach 75%, 72% and 79%.

As can be seen, our method performed better on

these three indicators and this method is most

suitable for feature screening.

Table 6: Results compared with other method.

Measure

Decision

Tree

Neural

Network

Method

RFSS

Sensitivity

61%

72%

75%

Specificity

78%

79%

78%

Accuracy

73%

75%

77%

Compared to other studies, the advantage of this

study is that the sample size is large and 8619 people

were used for modelling. Therefore, the method has

strong applicability. Another advantage of this

method is that the preselected physical indicators

had 32 indicators, which was large enough to screen

features. There are more iterations to increase the

amount of experiment, which can improve the

stability of the screening. However, there are some

shortcomings in the evaluation. As a medical data

study, we should also use statistical hypothesis

testing to consider the random fluctuation in the

results, so as to improve the accuracy and statistical

significance of the results. We will improve this part

in future studies.

The focus on this paper is to study how to screen

features, it is necessary to analyse why the accuracy

of method IV is not particularly high. Firstly, raw

data set was acquired from physical examination,

without specific data indicators like TOD, FHCD.

Table 5: The comparison of classification effect by using different screening methods.

Method

Feature Selection

Dimension

Algorithm

Accuracy

Sensitivity

Specificity

None

SVM

66%

54%

78%

PCC

SVM

75%

73%

78%

III

Random Forest

SVM

71%

62%

79%

RFSS

SVM

77%

75%

78%

IoTBDS 2019 - 4th International Conference on Internet of Things, Big Data and Security

With the development of Internet of Things

technology, patients will have more complete data,

and the accuracy rate will increase. On the other

hand, this SVM classifier was only used to judge the

screening effect of each method. As mentioned in

Chapter 2, the system classification module used

weighted algorithm. Therefore, the classification

effect of the whole system will be more accurate.

5 CONCLUSION

In this paper, we designed a risk factor screening

module using different screening methods based on

the proposed context-aware system for hypertension.

After comparison and improvement, we selected the

RFSS method combined by random forest and

stability selection in four methods. We gradually

filtered 32 parameters in context information

obtained from DA module to 13 hypertension risk

factors, and performed by SVM classification

algorithm. Accuracy, sensitivity and specificity have

been improved. This method can improve the

screening rate and ensure the accuracy of this

system. Therefore, the context-aware system of

hypertension will improve performance by using this

screening method. The output of the system can

assist doctors and patients to have a comprehensive

understanding of their blood pressure condition

according to risk factors.

In the future research, we will increase the

dataset and data parameters with the improvement of

the performance of portable devices. On this basis,

we will extend this work by applying DL

technologies such as CNN, in order to see whether

the accuracy can be increased. In addition, statistical

hypothesis testing will be added to experimental

verification and the results will be compared with

clinical practice. Finally, the automated hypertension

risk assessment approach will be improved based on

future study. The diagnosis of doctors will be more

accurate and comprehensive.

ACKNOWLEDGEMENTS

This work was supported by National Natural

Science Foundation of China (No.61471064), and

National Science and Technology Major Project of

China (No.2017ZX03001022).

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IoTBDS 2019 - 4th International Conference on Internet of Things, Big Data and Security