NOVEL HYBRID ARTIFICIAL INTELLIGENCE-BASED ALGORITHM TO DETERMINE THE EFFECTS OF AIR POLLUTION ON HUMAN ELECTROENCEPHALOGRAM SIGNALS

Abstract

Air pollution is a serious global issue. The complicated mix of gases, fluids, and particle matter, heterogeneous, and growing pollutants from cars, manufacturers, or houses, damage the ionosphere and also damage human health. In preliminary research, the risk for short and long-term exposure to coronary heart disease in existing levels of ambient particulate matter was consistently raised. These challenges can be modeled with Artificial intelligence (AI). Their benefit is that they can resolve the issue without the realization of the conceptual link between the input and output data under the situations of partial data. The AI approach may be used to accurately identify coronary
disease, and numerous diseases to prevent heart problems. This paper provides a comprehensive analysis of the data on air pollution and cardiovascular disease by healthcare experts and regulatory authorities and also differentiates individuals with cardiovascular disease from healthy individuals easily. The decision support system focused on AI Technology can help physicians better diagnose heart patients. When chosen by the Correlation-based function subset selection algorithm, t

Full text

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* For correspondence.
Journal of Environmental Protection and Ecology 22, No 5, 1825–1835 (2021)
Atmospheric pollution
NOVEL HYBRID ARTIFICIAL INTELLIGENCE-BASED
ALGORITHM TO DETERMINE THE EFFECTS OF AIR
POLLUTION ON HUMAN ELECTROENCEPHALOGRAM
SIGNALS
M. A. BERLINa*, NIRAJ UPADHAYAYAb, ALI ALGAHTANIc,
VINEET TIRTHc, SAIFUL ISLAMd, K. MURALIe,
PRAVIN R. KSHIRSAGARf*, BUI THANH HUNGg,
PRASUN CHAKRABARTIh, PANKAJ DADHEECHi
aDepartment of Computer Science and Engineering, RMD Engineering College,
Chennai,Tamil Nadu, India
E-mail: mba.cse@rmd.ac.in
bDepartment of Computer Science, J. B. Institute of Engineering and
Technology, 500 075 Telangana, India
cDepartment of Mechanical Engineering, College of Engineering, King Khalid
University, 61 411 Abha, Kingdom of Saudi Arabia; Research Centre for
Advanced Materials Science (RCAMS), King Khalid University, Guraiger,
61 413 Abha, Asir, Kingdom of Saudi Arabia
dDepartment of Civil Engineering College of Engineering, King Khalid
University, 61 413 Abha, Asir, Kingdom of Saudi Arabia
eDepartment of Electronics and Communication Engineering, Vijaya Institute
of Technology for Women, Enikepadu, Andhra Pradesh, Hyderabad, Telengana,
India
fDepartment of Electronics and Communication Engineering, AVN Institute of
Technology & Engineering, Hyderabad, Telengana, India
gData Analytics & Articial Intelligence Laboratory – DAAI Lab, Articial
Intelligence and Information System Programme, Institute of Engineering –
Technology, Thu Dau Mot University, 06 Tran Van On Street, Phu Hoa District,
Thu Dau Mot City, Binh Duong Province, Vietnam
hProvost and lnstitute Endowed Distinguished Senior Chair, Techno lndia NJR
Institute of Technology, 313 003 Udaipur, Rajasthan, lndia
iComputer Science and Engineering, Swami Keshvanand Institute of Technology
(SKIT), Management and Gramothan, 302 017 Jaipur, Rajasthan, India
Abstract. Air pollution is a serious global issue. The complicated mix of gases, uids, and particle
matter, heterogeneous, and growing pollutants from cars, manufacturers, or houses, damage the

1826
ionosphere and also damage human health. In preliminary research, the risk for short and long-term
exposure to coronary heart disease in existing levels of ambient particulate matter was consistently
raised. These challenges can be modelled with Articial intelligence (AI). Their benet is that they
can resolve the issue without the realisation of the conceptual link between the input and output data
under the situations of partial data. The AI approach may be used to accurately identify coronary
disease, and numerous diseases to prevent heart problems. This paper provides a comprehensive
analysis of the data on air pollution and cardiovascular disease by healthcare experts and regulatory
authorities and also differentiates individuals with cardiovascular disease from healthy individuals
easily. The decision support system focused on AI Technology can help physicians better diagnose
heart patients. When chosen by the Correlation-based function subset selection algorithm, the clas-
sier logistic regression for 20-fold cross-validation exhibited the greatest 92% accuracy.
Keywords: environmental factors, air pollution, cardiovascular diseases, Articial intelligence (AI),
correlation-based feature subset selection, relief feature selection algorithm, logistic regression, Naïve
Bayes, k-nearest neighbour algorithm.
AIMS AND BACKGROUND
Air pollution is an important risk factor in the breathing and cardiovascular systems
and environmental circumstances of human health. Receiving nationwide ambient
quality requirements for the preservation of public health and the environment,
different environmental protection authorities in different nations have recently
laid down1,2. The particulate matter (PM) (ozone at ground level (O3), carbon
monoxide (CO), sulphur oxides (SO2), nitrogen oxides (NOx) and lead as “critical
pollutants” are dened in the set. Among these six contaminants, O3 is one of the
most toxic emissions with detrimental impacts on human health and agricultural
development3. Several researchers in the eld of air pollution prediction have
recently been approved for the discovery and implementation of meteorological
modelling models, such as statistical models. Articial neural network (ANN), grey
model, and other hybrid techniques4–6. Governments, companies, and households
not only respond more promptly and economically to air pollution issues but also
acquire more knowledge into more expensive corrective actions6–8.
Kshirsagar et al.4,9,10 discussed about Electrocardiogram (ECG) is a bio-electric
signal that records the electric function of the heart over time. In the diagnosis of
cardiac illnesses and choice of suitable therapy for a patient, timely and precise
diagnosis is essential. Signal ECGs are utilised as parameters for cardiac disease
identication, with the majority of the data being derived from the MIT-BIH
dataset and Physio Data Net. The ECG signal is preprocessed using the toolbox of
the Wavelet and is also utilised to remove the ECG signal. Animesh Hazra et al.11
suggested a few of the existing research on cardiac disease prediction utilising data
mining methods analyses the many mixtures of mining algorithms employed and
determine what strategies are benecial and successful. In addition, certain pos-
sible improvements have been discussed in the prediction model. Suriya Beguma
et al.12 illustrated the application of several Heart Disease Dataset techniques is

1827
addressed. The Random Forest Classier machinery learning method has dem-
onstrated its accuracy and reliability in the suggested system. Skrzypski et al.1
suggested an air pollution prediction system based on articial neural networks.
The level of the toxic gases or a given range of levels can be predicted (class of
air quality)2. Comparable results recommend that air pollution is a key avoidable
cause of increasing respiratory problems and irregular heartbeats.
EXPERIMENTAL
Hazardous air pollutants cause heart disorders such as heart attack blockage (arte-
rial occlusion) and cardiac tissue death related to lack of oxygen, resulting in a
potential loss of heart (infarct formation). Various classication systems for the
diagnosis of cardiac disease have been checked for their success on complete and
certain functions. Selecting main features such as relief, maximum relevance –
minimum redundancy and least absolute shrinkage and selection operator has
been used and the efciency of the classiers has been checked on those features
extracted. Logistic regression, K-NN, and NB were used for common articial
intelligence classication11,12. The approach for the model validation and analysis
of the performance measures is estimated as shown further in Fig. 2.
A variety of possible methods have been developed to trigger immediate ef-
fects of air pollution on the cardiovascular system, plasma, and lung receptors,
as indicated in Fig. 1, or negative impacts caused by oxidative stress and chronic
inammation. Changed autonomy may also lead to arterial plaque destabilisation
and heart arrhythmias start. Severe cardiovascular reactions to PM exposure such
as myocardial infarction may lead to direct air-conditioning consequences1.
The particulate matters (PMs) are a collection of solid and liquid particles
which inuence human health signicantly based on their size (i.e. the lungs reach
PM10, diameter < 10 μm, and the pulmonary particles penetrate further into the
lung with diameters < 2.5 μm) (PM2.5) (Ref. 6).

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Air pollution (PM)
Z
Lung
inflammation
Blood
translocation
Autonomic
regulation
Heart Disease
PM
10
PM
2.5
Heart disease
database
Pre-processing
Feature extraction
and reduction
CFS
Relief
LASSO
AI algorithm
LR
KNN
NB
Model
prediction
Existence of Heart disease
Absence of Heart disease
K-fold cross-
validation method
Heart disease caused due
to Air pollution
Fig. 1. Block diagram of a hybrid intelligent system for predicting heart disease caused due to air
pollution
Database. The largest dataset available at the 2016 Physionet Challenge was used to
test the proposed technique. The Physionet database comprises six (A to F) datasets
containing 3240 raw heart sound samples in total. These samples were obtained
separately by various research groups in clinical and non-clinical (i.e. home visits)
settings using heterogeneous sensor devices from different countries9. The dataset
includes clean and noisy cardiovascular sounds. Both healthy individuals and
patients with several cardiac problems, in particular coronary artery disease and
heart valve disease, obtained the information. Themes included girls, adults, and
elderly people from various age groups. The sound registered in the heart ranged
between 5 and just 120 s (Ref. 11). The typical sounds in the heart normally con-

1829
sist of a rst sound (S1) and a second sound (S2). S1 arises when the mitral and
tricuspid valves close due to the sudden increase in pressure within the ventricles
at the start of the insulated ventricular contract. The aortic and pulmonary valves
are closed at the beginning of the diastole12.
Preprocessing of heart sound. The electronic stethoscope sound of the heart often
contains background noise. The preprocessing of cardiac sound is a critical phase
in the automatic detection of cardiac beat recordings. It exposes the underlying
physiological structure of the cardiac signal through the identication of anoma-
lies in the meaningful PCG signal regions and enables the automated detection of
pathological events7. The segmentation process is known as the detection of the
precise dates of the rst and second heart sounds (e.g. S1 and S2). The key objec-
tive of this procedure is to ensure that the incoming heartbeat is aligned correctly
before classication because it improves the recognition score signicantly. A
pattern of ECG beats is the input of this method. We take a few simple steps to
remove ECG beats from a given ECG signal4,5:
1. Standardisation of the ECG to the range from one to zero.
2. Find the t wave set in the Massachusetts Institute of Technology-Beth
Israel Hospital.arrhythmia database for the electrocardiogram R-peaks of their
consequent annotation le.
3. Separate the ECG continuous signal in a heartbeat sequence based on the
t waves obtained and attaches a marker on the annotation le for every heartbeat.
4. Redimensioning each heartbeat to a xed length (280 samples).
FEATURE EXTRACTION AND REDUCTION
One of the main steps in categorisation is to isolate and reduce features as the right
categorisation cannot be accurate even though features do not have to be selected.
This decreased dimensioning helps to reduce the database size while speeding up
the deduction procedure for a large database in particular. For that purpose, there
are various algorithms: Correlation-based feature subset selection (CFS); Relief
feature selection algorithm, and Least absolute shrinkage and selection operator.
Correlation-based feature subset selection (CFS): “A valuable functional subset
contains features that are not (not dened) one with the other, specically connected
to the class”13,14. A function evaluation form, based on test theory assumptions,
gives the estimation as dened in equation (1):
rfc = k rfc (1)
(k + k(k – 1) rff)1/2
where rfc – the correlation between the summed features and class variable; k –
the number of features; rfc – the average of correlation between features and class
variable; rff – the average inter-correlation between features.

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Continuous functions are converted into categorical attributes using supervised
as a preprocessing step before they are used as classication tasks to satisfy their
nominal or categorical and continuous or ordinal features in equation (1). The
information gain theory is used to estimate the extent of the association between
nominal characteristics. Furthermore, possible sub-sets of reduced features are
possible (n is initially the number of possible initial features). Particularly for a
large characteristic set, it would also be unrealistic to discover every sub-set to nd
the best subset Also, in different applications, both lter-type and wrapper-type
feature selection methods use correlation-based approaches.
Relief feature selection algorithm. Relief is a distance-based method used to assess
feature weight in the calculation of the angle from a randomly selected example
(nearest hit) by observing the variation in functional values while calculation the
distance between a randomly selected case and its closest case, provided it has a
different value on the same feature as calculating the nearest distance the greater
the chance of the closest miss’ distance, the greater the weight of the feature, but
the opposite, the weight of the characteristics is inversely proportional to the prob-
ability of the nearest hit’s distance. A relief algorithm is described here, where the
weight of Feature X is targeted and denoted by D[U] in equation (2):
D[U] = P(different value of X | nearest instance from the different class) –
P(different value of X | nearest instance from same class). (2)
Least absolute shrinkage and selection operator. Using the last absolute reduction
and selection operator to pick features by upgrading the absolute importance of
the features. The LASSO works superbly with low coefcients14. AI classica-
tion techniques are used to identify heart patients and healthy individuals. Some
popular classication algorithms are discussed briey with their theoretical basis:
Logistic regression; Naïve Bayes; k-nearest neighbour.
Logistic regression. The logistic HSMM regression is similar to an SVM-based
probability of emission and allows more segregation in various states. Logistic
regression is a binary classication that uses a logistic function to map the func-
tion space or prediction variables to the binary response variables7,9,11. The logistic
function σ(α) is dened as:
σ(α) = 1/(1 + exp(–α)). (3)
Within the logistic function, it is possible to dene the probability of a state
or class given the input analyses Ot :
P[qt = α|Ot] = σ(w′Ot), (4)
where w is the weights of the framework used for every analysis or input. The
classier is constructed and weighted on the dataset on lesser squares10. The prob-

1831
ability of each analysis given the state for the one-versus-all logistic regression
bj(Ot|αj) is found by using the Bayes’ rule:
bj(Ot) = P[Ot|qt = α] = P[qt = α|Ot] × P(Ot). (5)
P(αj)
The P(Ot) can be measured from a normal multivariate allocation of the entire
training data, and P(α j) is the initial allocation of the probability.
Bayesian networks. A Naive Bayes (NB) is a technique for training. The theorem
determined the form of new factor variables in terms of the highest probability.
The NB uses data sets to evaluate a certain class’s vector likelihood5,13. Depend-
ing on its likelihood value for individual vector the development work class is
determined. For the grouping of texts, NB is used. Then the most possible class
is allocated test text using the Bayes rule:
G(A|X) = G(X|A) G(A), (6)
G(X)
where G(A|X) is the posterior probability of class; G(A) – the prior probability of
class; G(X|A) – the likelihood which is the probability of the predictor given class,
and G(X) – the prior probability of predictor.
k-Nearest neighbour. The k-nearest neighbour approach (KNN) denes the clos-
est neighbour about the k value dening the number of closest neighbours to be
seen to identify the data point class14,15. The structure-based approach covers the
fundamental structure of data with less mechanical data training. In a less techni-
cal structure, all the information is divided into datasets and training data, and
the distance between the sampling points and all the training points is calculated.
RESULT AND DISCUSSION
This sector addresses diverse perspectives on the processes and consequences of
classication. We rst evaluated the efciency of different articial intelligence al-
gorithms, including logistical regression, KNN, and Naive Bayes, to set up complete
heart disease results. In the second, CFS, Relief, and LASSO selection algorithms
were used for essential features. Third, classiers also measured the usefulness of
chosen features. The k-fold method was often used for cross-validation. The clas-
sication algorithms were applied to check the output assessment methods. This
research carried out tests with k = 12 for the KNN classiers. At k = 12, however,
the efciency of KNN as shown in Fig. 2 was excellent.

1832
Fig. 2. Performance of KNN for different values of k
The relation between temperature, humidity, heart rate, and various risk level,
and human comfort decisions that can be applied to the constructed continuous
monitoring system is described in Table 1. 20-fold CV category metrics of heart-
disease algorithms is described in Table 2.
Table 1. Boolean decision-making table based on patient status
Tem -
perature
sensor
(oC)
Humidity
in (%age)
Human perception Pulse rate sensor
(per min)
Action taken Level of
risk
<37 31–41 comfortable 60 to 100 no action 1
37–38 41–46 comfortable for
many people
40–60 or 100–120 informed fam-
ily members
2
>38 46–52 uncomfortable for
many people at up-
per age
40–60 or 100–120 informed to a
local doctor
3
>38 >52 high humid, ex-
tremely uncomfort-
able
<40 or >120 emergency 4
Table 2. 20-fold CV category metrics of heart-disease algorithms
Predictive model Accuracy Specicity Sensitivity
Linear regression 92.00 89.98 88.12
Naive Bayes 89.34 92.45 86.45
KNN 85.57 88.01 84.56

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Fig. 3. Accuracy performance of various classiers with 20-fold CV
Fig. 4. Specicity performance of various classiers with 20-fold CV
Fig. 5. Sensitivity performance of various classiers with 20-fold CV
Fig. 6. Performance of various classiers with 20-fold CV

1834
The predictive performance of LR was 92% (accuracy) as shown in Fig 3,
88.12% (sensitivity) as shown in Fig 5, and 89.98% (specicity) as illustrated in
Fig 4. The second classier was NB that has 92% (specicity), 86.45% (sensitivity),
and 89.34% (precision). K-NN, which has 88.01% (precision), 84.56% (accuracy),
and 85.57% (accuracy) of the classication was the third important classication.
The efciency of 20 fold CV classiers on the maximum range is shown in Fig.
6. As shown in it, LR’s output in terms of precision, sensitivity, and specicities
beats the other two classiers.
CONCLUSIONS
Air pollution is a furious worldwide problem. Increased cars, industry, and home
pollutants are detrimental to the atmosphere and impact human lives as well. The
device was checked on a dataset of Cleveland cardiac disease. Three popular clas-
siers, for example, logistic regressions, KNN, and NB have been used for select-
ing the essential characteristics with the three practical algorithms relief, CFS and
LASSO. In the validation scheme, the k-fold cross-validation approach is used.
Different calculation methods were often introduced to verify the efciency of clas-
siers. When chosen using a correlation-based subset sorting algorithm, the logistic
regression with 20-fold cross-validation displayed the highest accuracy of 92%.
Acknowledgements. The authors thankfully acknowledge the Deanship of Scientic Research, King
Khalid University (KKU), Abha, Asir, Kingdom of Saudi Arabia for funding this research under
the grant number R.G.P.2/89/41. This work has been done remotely at DAAI Lab, Thu Dau Mot
University Vietnam and i3 LABs, Techno India NJR Institute of Technology, India.
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Received 26 May 2021
Revised 25 June 2021