Computers, Materials & Continua
DOI:10.32604/cmc.2022.019790
Article
Tech Science Press
Optimized Tuned Deep Learning Model for Chronic Kidney
Disease Classification
R. H. Aswathy1, * , P. Suresh1 , Mohamed Yacin Sikkandar2 , S. Abdel-Khalek3, Hesham Alhumyani4,
Rashid A. Saeed4 and Romany F. Mansour5
1
Department of Computer Science and Engineering, KPR Institute of Engineering and Technology,
Coimbatore, 641407, India
2
Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University,
Al Majmaah, 11952, Saudi Arabia
3
Department of Mathematics, College of Science, Taif University, Taif, 21944, Saudi Arabia
4
Department of Computer Engineering, College of Computers and Information Technology, Taif University,
Taif, 21944, Saudi Arabia
5
Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt
*
Corresponding Author: R. H. Aswathy. Email: rhaswathy@gmail.com
Received: 25 April 2021; Accepted: 06 June 2021
Abstract: In recent times, Internet of Things (IoT) and Cloud Computing
(CC) paradigms are commonly employed in different healthcare applications.
IoT gadgets generate huge volumes of patient data in healthcare domain,
which can be examined on cloud over the available storage and computation
resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the
deadliest diseases that has high mortality rate across the globe. The current
research work presents a novel IoT and cloud-based CKD diagnosis model
called Flower Pollination Algorithm (FPA)-based Deep Neural Network
(DNN) model abbreviated as FPA-DNN. The steps involved in the presented
FPA-DNN model are data collection, preprocessing, Feature Selection (FS),
and classification. Primarily, the IoT gadgets are utilized in the collection
of a patient’s health information. The proposed FPA-DNN model deploys
Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal
subset of features from the preprocessed data. The application of FPA helps
in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the
benchmark CKD dataset. The results were examined under different aspects.
The simulation outcomes established the superior performance of FPA-DNN
technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%,
accuracy of 98.75%, F-score of 99%, and kappa of 97.33%.
Keywords: Deep learning; chronic kidney disease; IoT; cloud computing;
feature selection; classification
This work is licensed under a Creative Commons Attribution 4.0 International License,
which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.
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1 Introduction
Internet of Things (IoT) is commonly employed in the integration of applications with computer systems. IoT is applied in a massive number of applications that consumes minimum energy,
for example, refrigerator, smart watch, wrist band etc., rather than in high power consumption
devices such as monitors, tablets, tools, mobile phones etc. In recent times, few home appliances
like room freshener unit and air conditioners are operated by microcontroller along with sensing
devices which result in practical outcomes. On a progressive note, when IoT is integrated with
Cloud Computing (CC) model, it achieves high efficiency and is beneficial with massive number of
standard features. In comparison with other sectors, clinical sector is one of the most promising
areas that needs rapid development of clinical and sensing devices [1]. With medical devices
getting costlier, most of the critical diseases are not predicted or treated at early stages. These
are highly important and mandatory processes to save a human life. The proposed research is
highly concentrated on Clinical Decision Support System (CDSS) through internet which applies
IoT devices to verify the existence of severe diseases in human beings. IoT collects a number of
details in terms of clinical applications whereas the data science paves an essential way to leverage
the IoT for beneficial outcomes.
At present, Deep Learning (DL) models are implemented in various domains for pattern
identification and new models are also being developed from the collected information. Machine
Learning (ML) is one of the well-known and significant models in CDSS that can handle largescale data. Under the application of data analysis methods, data types can be defined in terms
of Variety, Volume and Velocity. Neural Network (NN) is one of the effective classifiers [2] which
invoke a novel technique with the help of CC and IoT methodologies to predict a patient’s current
state and disease condition. Healthcare details are collected and stored in significant UCI data
repository whereas sensing gadgets are developed to broadcast the data on affected region for
students in digital fashion. Different classification methods have been applied in the prediction
of diseases in terms of F-measure, sensitivity and specification. This approach is effective in
comparison with conventional models. Li et al. [3] deployed a novel energy method that can
be operated in cloud-based IoT platform. It was developed to examine the images and videos
recorded by a camera fixed on to the vehicles. This method is valuable in practical applications
and computes the simulation by applying familiar simulation tool to observe the enhancements
using IoT devices.
Stergiou et al. [4] evaluated the application of CC and IoT methods to solve the problems
relevant to security. Tao et al. [5] deployed a new multi-layer cloud platform to enable proficient
and prominent communication and multitasking compared to non-identical service generated by
diverse agents in modern city. Moreover, ontology was created to solve the heterogeneity problems
found in layer cloud platform. Kumar et al. [6] executed a new 3-tier structure for recording
massive number of sensor details. Of the three layers, Tier-1 focuses on developing the data
gathered from distinct sources. Then, Tier-2 deals with numerous sensor details saved in the cloud
server. Finally, Tier-3 is a predictive model used for Heart Disease (HD) prediction. Dehury
et al. [7] proposed a novel approach for real-time cloud management and technical data which
is not related to IoT. Lee [8] introduced an efficient and useful Cyber Physical System (CPS)
model for massive number of applications. Chen [9] labelled a modern framework in car camera
system which applies mobile CC model in Deep Learning (DL) method. This framework is highly
proficient in the identification of objects that exist in the recorded video while driving and is
helpful in decision making process. Further, the data gets stored in cloud environment which
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reduces the usage of local storage space. Hence, this method is classified as Training, Recognizing,
and Data collection model.
Wu [10] introduced a novel cloud-based model with ML technique for machinery prognostics.
It makes use of Random Forest (RF) classifier to predict dry mill operations. Furthermore, RF
model was presented under the application of MapReduce and was implemented on Amazon
cloud environment. The proposed model ensured the application of RF classifier in accurate
diagnosis of the disease. Muhammad et al. [11] conducted a research to track human voice
pathology under the application of cloud and IoT. A new prediction approach was established on
the basis of Local Binary Pattern (LBP) and was applied to observe human voice pathology. Gope
et al. [12] presented a novel technique in body area sensor network on the basis of IoT devices.
Using this model, the diseased person can be observed prominently with the help of compact and
sensational sensor networks. Healthcare framework generally requires effective security mechanism.
An online healthcare monitoring system was developed by Hossain et al. [13] to examine the
health of patient and collect his or her details in order to find the reason behind death. Security
methods such as watermarking and signal enhancements were combined in this study to remove
the medical errors and find the existing attacks.
Significant developments that occurred recently in the medical domain of Chronic Kidney
Diseases (CKD) have to be examined and such studies have to be conducted for a long time to
understand disease prognosis. Some of the chronic illnesses associated with progressive CKD are
stroke, diabetes, tumor, etc. Early prediction of chronic disease is important since it invokes a
preventive measure for better treatment. Here, IoT and cloud-related CDSS are presented using
computational science. With the application of IoT gadgets in medical field, the equipments are
utilized frequently to gather details about healthcare parameters over a period of time and to
verify the existence of abnormalities in short duration. Moreover, IoT and healthcare sensor
measurements are employed effectively in the prediction of disease progression within a limited
time interval. Personal medical services make use of IoT and cloud so that the lifetime can
be expanded at minimum expense. Therefore, effective healthcare model is essential for disease
examination using clinical devices.
The current research work presents a novel IoT and cloud-based CKD diagnosis model called
Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as
FPA-DNN. The presented FPA-DNN model comprises of data collection, preprocessing and
Feature Selection (FS) as primary steps after which the classification is performed as the final
step. Mainly, IoT gadgets are utilized to gather details about a patient’s health. Afterward, the
data is preprocessed following three ways namely, format conversion, data transformation and
missing value replacement. In addition, the FPA-DNN model involves Oppositional Crow Search
(OCS) algorithm for FS i.e., selection of optimal feature subset. The application of FPA helps in
tuning the DNN parameters for better classification performance. The simulation analysis of the
presented FPA-DNN model was conducted against CKD database and the experimental outcome
is examined in detail in the upcoming sections.
2 The Proposed FPA-DNN Technique
The overall process of FPA-DNN is displayed in Fig. 1.
The presented FPA-DNN model comprises of data collection, preprocessing and Feature
Selection (FS) as primary steps after which the classification is performed. The data collection
component has the potential to collect essential details from CDS. Moreover, the collected data is
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pre-processed and FS steps enhance the supremacy of the information. Then, FPA-DNN method
is implemented to train the OMDSS. In order to validate the approach, the projected FPA-DNN
model is simulated through online mode with clinical data to check whether it can classify the
data as either CKD or non-CKD.
Figure 1: The overall process of the proposed FPA-DNN model
2.1 Data Collection
In this stage, three types of data are collected. The current framework enables the collection
of patient information under the application of IoT devices. In general, the sensor linked to the
human collects certain medical data for a limited duration. The newly-proposed OMDSS exploits
4G network for transferring the monitored data to Cloud Database Server (CDS).
2.2 Preprocessing
In this stage, the medical dataset is converted to a meaningful format under the application
of three different ways. At first, the format conversion is performed during when the data is
transformed from its actual format into .csv file format for further computations. Followed by, the
categorical value in the database is converted to mathematical values such as ‘0’ and ‘1’. Finally,
the missing values exist in the dataset are occupied by median model.
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2.3 OCS Based FS Model
In this stage, the optimal subset of features is selected using OCS algorithm from the preprocessed data. Raj et al. [14] introduced the CS model based on the natural hierarchy of crows
in terms of hiding and food grabbing. It is also developed based on the structure of a flock,
preservation of food hiding locations by crows, stealing the food, and following one another. By
probability, a crow saves its caches from getting stolen. Both actual and novel locations of two
crows are illustrated in Fig. 2. In order to make a CS model effective, a contrast operation is
included in this model. For all initialized solutions, the adjacent opposite operation is initialized.
When the solutions are compared, the best solution is selected as the optimal one. The execution
strategy of OCS model is defined in a step-by-step manner herewith.
Figure 2: The general model of the CS representation
In population initialization stage, the population of crows is allocated by means of Fi , while
the initialized crows (features) are randomly placed in a search area.
Fi = F1 , F2 , . . . , Fn ,
where i = 1, 2, 3, . . . , n
(1)
In general, the meta-heuristic optimization methods are invoked with initial solutions which
further attempts at enhancing a solution simultaneously. After comparing the solutions, the best
one is decided as the initial solution. For sample, consider f ∈ (g, h) which implies a real number.
Using an opposite point definition, it is defined as follows.
f̃j = gj + hj − fj
(2)
Fitness Function (FF) of OCS approach is determined based on the objective function. In
this model, the involvement is present to reach the best features from the applied database images.
OFi = MAX (Accuracy)
(3)
A crow forms a new position randomly by selecting a single crow among a flock of crows,
where crow ‘j’ should have its own position and memory space. The intervention position of a
crow Pi, iter is accomplished by the given Eq. (4).
⎧
⎨Pi, iter + ri × fl i, iter × memj, iter − Pi, iter if rj ≥ APj, iter
Pi,iter+1 =
(4)
⎩
randP
otherwise
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The extension of Eq. (4) is defined as: ri and rj which are nothing but random values of
crows, i and j correspondingly between [0 and 1], fl i, iter implies the flight crow length i, P signifies
the place of a crow, menḋ refers to the memory position of j th crow and APj, iter denotes the
effective possibility of crow j at iteration.
The recently updated crow’s position and storage space values are improved using Eq. (5).
⎧
⎨Pi, iter
f (Pj, iter+1 ) > f (memj, iter )
i, iter+1
mem
(5)
=
⎩
memi, iter otherwise
It is observed that the fitness value for novel location of the crow is excellent in the applied
location. The crow enhances the memory by new position. When maximum number of iterations
is attained, the optimum position of the memory resembles the objective which is reported as the
optimal solution of the extracted features. The flowchart of OCS model is shown in Fig. 3.
Figure 3: The flowchart of OCS
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2.4 DNN Based Classification
Generally, DL models are applied to achieve high dimension features from input database.
Hence, the features gathered from DNN are used to improve the performance of classifiers. DNN
classification model is applied prominently through the integration of a stack of auto encoders
(AE) system, a result of application of SM classifier [15]. AE is comprised of input, hidden
and output layers and is trained in an unsupervised manner to generate an equivalent input at
consequent stage with limited erection error. Hence, the intensity of the outcome is similar to the
intensity of the input. In addition, AE is trained to embed the input into feature space which
contributes a low dimension over input space. Thus, the dimensions of the code space are chosen
as maximum over input space in order to improve the effectiveness of the classification in special
events. Here, AE aims at providing the best presentation of the input vector by replacing proper
codes.
Both input as well as output associations of an encoder are expressed as c = gE (W, b; u) and
is demonstrated as Eq. (6):
(6)
c = f b + WZ u
where ‘f’ denotes the activation function of an encoded neuron. Then, the weight of the encoding
unit is represented as W, which connects the input of hidden layer and b vector with neuron
bias. The u vector is described as an input of encoder portion whereas vector c is meant to be
the result of encoding unit. The correlation between input and output of the encoder is depicted
herewith.
û = f̂ b̂ + Ŵc
(7)
In this model, f̂ indicates the activation function of decoder neuron. The input output
association of a decoder is expressed as û = gD (Ŵ, b̂; c). The result of AE is illustrated by
û = gAE (W, b, Ŵ, b̂; u). The objective function of AE is defined as follows.
N
Esparse = EZ + β
KL(ρ ρ̂
q=1
(8)
The pre-defined cost function is composed of two parts. At the initial stage, EZ is meant to
be an objective function of NN, whereas β denotes the weight of a sparsity penalty in Eq. (8):
1
EZ =
Z
Z
e2k +
k=1
λ
W + Ŵ
2
(9)
where λ denotes the regularization term which is highly used in the elimination of overfitting
issues. Error vectors can be described as the difference between desired results and the actual
output as showcased herewith.
ek = u(k) − û
(10)
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where k = 1, 2, . . . , Z. It is an elegant model to observe that EZ denotes inner weight of the AE
where,
(11)
EZ = EAE W, b, Ŵ, b̂
The secondary part of Eq. (8) is expressed as follows.
KL(ρρ̂q ) = ρ log
ρ
1−ρ
+ (1 − ρ) log
ρ̂q
1 − ρ̂q
(12)
where ρ implies the sparsity value and ρ̂ is provided below:
ρj =
1
Z
Z
fq (u(i) )
(13)
p=1
The AE components are linked together for developing a Stacked AE (SAE) system. The
encoder is comprised of various AEs which are connected together for deploying SAE. By reforming input and output correlations of AE, the SAE network with L-cascaded AEs is accomplished
as provided below.
gSAE = g1E og2E o · · · ogL
E
(14)
SAE approach is deployed by encoding unit of the AE, whereas the decoder parts of the AE
are not used in the development of SAE. Finally, the training is completed by tuning the process
which in turn improves the performance of DNN classifier under the application of FPA.
2.5 FPA Based Tuning Process
In FPA [16], pollination is a process carried out in plant generation principle. Flower and
pollen gametes intend to offer a scalable solution to overcome specific optimization problems.
Flower constancy is selected as the best solution and is a reliable one. In global pollination,
the pollinator sends the pollen from long distance to higher fitting, whereas local pollination is
performed in smaller regions with unique flower in shaded water. Global pollination is carried out
with an opportunity termed as ‘switch probability’.
If a stage is removed, the local pollination undergoes replacement. In FPA, four rules are
applied in general which are given herewith.
• Live and cross-pollination are named as global ones in which the pollen pollinator makes
use of the Levy flight method.
• Abiotic and self-pollination are meant to be local pollination.
• Insects are referred to as the Pollinators that are suitable in making flower fidelity.
• The interaction between global and local pollination is adjusted using a switch likelihood.
Therefore, both 1st and 3rd rules are described as follows.
xqt+1 = xtp + γ × L (λ) × g∗ − xtp
(15)
where xtp implies the pollen vector at iteration t; g∗ denotes the recent optimal solution; γ = a
refers to a scaling factor for managing step size; and L represents the efficiency of pollination
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relevant to the step size for levy distribution [17]. Next, L means a levy distribution as illustrated
herewith.
L∼
λ × Ŵ (λ) × sin π2λ
1
× 1
S ≫ S0 0
π
S +λ
(16)
where Ŵ (λ) represents a normal gamma function. Under local pollination, both 2nd and 3rd rules
are applied as given herewith.
(17)
xpt+1 = xtp + ε xtq − xtk
where xtq and xtk denote the pollens from different flowers of the same plant.
Figure 4: Flowchart of the FPA model
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In numerical form, if xtq and xtk emerge from the same plant and are selected from similar population, then it is named as ‘local random walk’ whereas ε is composed of uniform
distribution from [0, 1]. Fig. 4 demonstrates the workflow of FPA.
3 Performance Validation
The efficiency of the proposed FPA-DNN can be ensured by applying the model widely in
different domains. The subsequent sections examine the dataset, estimation attributes and the
accomplished outcomes. The simulation outcomes are applied with respect to diverse evaluation
parameters.
3.1 Dataset
The outcomes of the FPA-DNN were investigated using a standard original CKD dataset.
The dataset is composed of 400 instances with 24 features [18]. A collection of two classes i.e.,
positive and negative class labels are present in the database. For experimental investigation, 10fold cross validation method was applied.
3.2 Results Analysis
The results achieved by OCS-FS model were compared with existing FS models in terms
of best cost. The chosen features are listed in Tab. 1 and Fig. 5. The table values portray that
the CFS algorithm depicted an ineffective FS performance and attained the highest best cost of
0.79. Followed by, the PCA model demonstrated a moderate FS outcome with its best cost being
0.4570. Though the PSO-GS and GA-FS models achieved reasonable best costs such as 0.03656
and 0.03440, the OCS-FS model outperformed other FS models with an optimal best cost of
0.00986. It is also displayed that the OCS-FS algorithm selected a set of 12 features out of 24,
i.e., 2, 4, 5, 8, 10, 12, 13, 14, 16, 19, 20, and 22.
Table 1: The result analysis of FS methods for chronic kidney disease dataset
Methods
Best cost
Selected features
OCS-FS
PSO-FS
GA-FS
PCA
CFS
0.00986
0.03656
0.03440
0.04570
0.79000
2, 4, 5, 8, 10, 12, 13, 14, 16, 19, 20, 22
15, 12, 24, 23, 13, 20, 11, 8, 18, 3, 9, 1, 14, 5, 2, 6, 17, 19
16, 24, 13, 9, 14, 17, 22, 19, 2, 15, 23, 18, 12, 6, 4, 10, 3, 20
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
4, 6, 7, 10, 15, 17, 19, 22
Fig. 6 shows the confusion matrix generated by FPA-DNN model during the execution time.
From the figure, it is apparent that the FPA-DNN model classified a total of 248 and 147
instances under positive and negative classes respectively.
Fig. 7 shows the comparative analysis of FPA-DNN method against previous models [19–21]
in terms of sensitivity and specificity. The figure implies that the Olex-GA approach performed
poor in terms of diagnosis and reached a minimum sensitivity of 80% and specificity of 66.66%.
Along with this, the LR technology represented a better result i.e., sensitivity of 83% and specificity of 82%. Moreover, the XGBoost approach showcased moderate and identical sensitivity
and specificity values of 83% each. In line with this, the PSO technology produced considerable
results with a sensitivity of 88% and a specificity of 80%. Besides, the ACO approach attained
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an acceptable sensitivity of 88.88% and a specificity of 84.61%. Moreover, the DT framework
illustrated reasonable results with a sensitivity of 90.38% and a specificity of 89.28%. Followed by,
the MLP technology showcased a slightly moderate function with its sensitivity being 92.3% and
specificity being 92.86%. The FNC method, on the other hand, implied the least outcomes with
a sensitivity of 95.68% and a specificity of 95.86%. Furthermore, the D-ACO scheme depicted
competent results with a sensitivity of 96% and a specificity of 93.33% respectively. Finally,
the FPA-DNN framework provided supreme function with the highest sensitivity of 98.8% and
specificity of 98.66%.
Figure 5: Best cost analysis of OCS-FS with other FS models
Figure 6: Confusion matrix of proposed FPA-DNN
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Figure 7: Results analysis of FPA-DNN with other models. (a) Sensitivity, (b) Specificity
Figure 8: F-score and kappa analysis of FPA-DNN with existing models
Fig. 8 presents the results of comparative analysis of FPA-DNN technology against traditional models with respect to F-score and kappa. The figure portrays that the LR approach
showcased ineffective diagnostics and accomplished a minimum F-score of 79% and a kappa of
74.6%. Likewise, the OlexGA technique represented moderate results with an F-score of 80% and
a kappa of 46.66%. In addition, the XGBoost model depicted moderate F-score and kappa values
such as 80% and 75.42% respectively. In line with this, the PSO model managed to represent
a considerable outcome with an F-score of 88% and a kappa of 68%. Then, the ACO scheme
attained a better F-score of 90.56% and a kappa of 72.06%. Moreover, the DT framework
depicted acceptable results with an F-score of 92.15% and a kappa of 78.37%. Next, the MLP
scheme showcased slightly improved results with an F-score of 94.11% and a kappa of 83.78%.
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Additionally, the D-ACO method exhibited slightly better results with an F-score of 96% and a
kappa of 89.33%. The FNC model illustrated competing results with an F-score of 96.63% and
a kappa of 90.87% respectively. Lastly, the FPA-DNN model outperformed all other methods by
producing the maximum F-score of 99% and a kappa of 97.33%.
Fig. 9 shows the results of accuracy analysis for the presented FPA-DNN model against
other models. The figure showcases that the Olex-GA technique is a poor diagnostic performer
as it attained the least accuracy of 75%. Additionally, LR and XGBoost models displayed better
accuracy values such as 82.0% and 83.0% correspondingly. Followed by, PSO and ACO techniques
reached moderate accuracy values of 85% and 87.5% correspondingly. Besides, the DT and
MLP models depicted reasonable performance with its accuracy values being 90% and 92.5%
correspondingly. Moreover, D-ACO and FNC approaches demonstrated near optimal performance
with accuracy values such as 95% and 95.75% correspondingly. At last, the FPA-DNN model
offered a superior performance and achieved the highest accuracy of 98.75%.
Figure 9: Accuracy analysis of FPA-DNN with existing models
4 Conclusion
The current research article designed a novel IoT and cloud-based CKD diagnosis model
named FPA-DNN model. The presented FPA-DNN model comprises of different stages such
as data collection, preprocessing, FS and classification. Primarily, IoT gadgets are utilized to
gather details about a patient’s health information. The FPA-DNN model makes use of OCS
algorithm for FS in which an optimal subset of features is selected from the preprocessed
data. The application of FPA helps in tuning the DNN parameters for better classification
performance. The presented FPA-DNN model was experimentally evaluated against a benchmark
CKD dataset. The obtained results verified that the FPA-DNN model is a superior performer as
it achieved the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score
of 99%, and kappa of 97.33%. In future, the outcome of the FPA-DNN model can be enhanced
using clustering and outlier removal techniques.
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Funding Statement: This research was supported by Taif University Researchers Supporting Project
Number (TURSP-2020/216), Taif University, Taif, Saudi Arabia.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding
the present study.
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