Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid 19 Pandemic Crisis

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 6, September-October 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD46400 | Volume – 5 | Issue – 6 | Sep-Oct 2021 Page 230
Health Risk Prediction Using Support Vector Machine with
Gray Wolf Optimization in Covid-19 Pandemic Crisis
Swati Shilpi1
, Dr. Damodar Prasad Tiwari2
1
PG Scholar, 2
Assistant Professor,
1,2
Department of CSE, BIST, Bhopal, Madhya Pradesh, India
ABSTRACT
The opinion of disease is important for Covid 19 as the antigen kit
and RTPCR are unperfect and should be better for diagnosing such
disease. Real-Time Return Transcription (real-time converse
transcription – polymerase chain). Healthcare practices include the
collection of various sorts of patient data to help the physician
diagnose the patient's health. These data could be simple symptoms,
first diagnosis by a doctor, or an in-depth laboratory test. These data
are therefore used for analyses only by a doctor, who subsequently
uses his particular medical skills to found the ailment. In order to
classify Covid 19 disease datasets such mild, middle and severe
diseases, the proposed model utilizes the notion of controlled
machine education and GWO-optimization to regulate if the patient is
affecting or not. An efficiency analysis is calculated and compared of
disease data for both algorithms. The results of the simulations
illustrate the effective nature and complexity of the data set for the
grading techniques. Compared to SVM, the suggested model
provides 7.8 percent improved prediction accuracy. The prediction
accuracy is 8% better than the SVM. This results in an F1 score of 2
percent better than an SVM forecast.
KEYWORDS: Covid-19, Pneumonia, Machine Learning, Artificial
Intelligence, Healthcare
How to cite this paper: Swati Shilpi |
Dr. Damodar Prasad Tiwari "Health
Risk Prediction Using Support Vector
Machine with Gray Wolf Optimization
in Covid-19
Pandemic Crisis"
Published in
International Journal
of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-5 | Issue-6, October 2021,
pp.230-234, URL:
www.ijtsrd.com/papers/ijtsrd46400.pdf
Copyright © 2021 by author(s) and
International Journal of Trend in
Scientific Research and Development
Journal. This is an
Open Access article
distributed under the
terms of the Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
I. INTRODUCTION
HRA is one of the critical pieces of custom drug that
assesses the general strength of an individual and the
shot at having a given sickness. Given the procedure
with relationship between fundamental thought
subject matter experts and patients, an assistant would
be ordinarily prepared to ensure proper HRA
gathering. Crucial HRA data covers economics,
lifestyle, history and physiological data for individual
and family prosperity, (for instance, beat, weight,
cholesterol, etc) For example, the USPSTF proposes
various procedures for the countering of chest
sickness danger, recalling different degrees of
information for family parentage.
In specific conditions the circuit of genetic
information might chip away at additional risk
assessments and possibly further foster countering,
assumption and treatment. The PALB2 is an amazing
portrayal of how to refine the risk information, in this
model family parentage, by planning inherited data
with standard peril information. Given the
significance of fundamental thought in keeping an
eye on the overall success of the patient, convincing
evaluation of prosperity danger and family parentage
will probably further develop risk layering and joint
clinical decisions with other clinical providers. This
will further develop clinical consideration transport
across the prosperity system.
II. PREVIOUS WORK
There are a couple of papers which have been
considered and insinuated on my work.
Covid Disease 2019 (COVID-19) spread
internationally in mid-2020, making the world face
an existential wellbeing emergency. Mechanized
recognition of lung contaminations from registered
tomography (CT) pictures offers an incredible
potential to expand the conventional medical
services technique for handling COVID-19. In any
case, sectioning contaminated districts from CT
cuts faces a few difficulties, remembering high
variety for disease attributes, and low force
IJTSRD46400

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
contrast among contaminations and ordinary
tissues. Further, gathering a lot of information is
illogical inside a brief time frame period,
restraining the preparation of a profound model. To
address these difficulties, a novel COVID-19 Lung
Infection Segmentation Deep Network (Inf-Net) is
proposed to naturally recognize contaminated
locales from chest CT cuts. In our Inf-Net, an equal
incomplete decoder is utilized to total the
undeniable level highlights and produce a
worldwide guide. Then, at that point, the implied
turn around consideration and express edge
consideration are used to show the limits and
improve the portrayals. Also, to reduce the
deficiency of named information, we present a
semi-directed division system dependent on an
arbitrarily chosen proliferation methodology,
which just requires a couple of marked pictures and
use fundamentally unlabeled information. Our
semi-administered structure can improve the
learning capacity and accomplish a better. Broad
tests on our COVID SemiSeg and genuine CT
volumes exhibit that the proposed Inf-Net beats
most state of the art division models and advances
the best in class execution (Deng-Ping Fan, Tao
Zhou, Ge-PengJi, Yi Zhou, Geng Chen, Huazhu
Fu, JianbingShen and Ling Shao; 2020)
Covid illness 2019 (COVID-19) is a pandemic
brought about by novel Covid. Coronavirus is
spreading quickly all through the world. The
highest quality level for diagnosing COVID-19 is
converse record polymerase chain response (RT-
PCR) test. Notwithstanding, the office for RT-PCR
test is restricted, which causes early conclusion of
the illness troublesome. Effectively accessible
modalities like X-beam can be utilized to recognize
explicit indications related with COVID-19. Pre-
prepared convolutional neural organizations are
generally utilized for PC helped identification of
illnesses from more modest datasets. This paper
examines the viability of multi-CNN, a mix of a
few pre-prepared CNNs, for the robotized
identification of COVID-19 from X-beam pictures.
The strategy utilizes a mix of highlights extricated
from multi-CNN with connection based element
choice (CFS) method and Bayesnet classifier for
the expectation of COVID-19. The strategy was
tried utilizing two public datasets and
accomplished promising outcomes on both the
datasets. In the first dataset comprising of 453
COVID-19 pictures and 497 non-COVID pictures,
the technique accomplished an AUC of 0.963 and
an exactness of 91.16%. In the second dataset
comprising of 71 COVID-19 pictures and 7 non-
COVID pictures, the technique accomplished an
AUC of 0.911 and an exactness of 97.44%. The
trials acted in this investigation demonstrated the
viability of pre-prepared multi-CNN over single
CNN in the discovery of COVID-19. (Bejoy
Abraham, Madhu S. Nair; 2020)
This paper proposes a three-stage Susceptible-
Infected-Recovered-Dead (3P-SIRD) model to
compute an ideal lockdown period for some
particular topographical locales that will be ideal to
break the transmission chain as well as will assist
country's economy with recuperating and backing
foundation in a battle against COVID-19. Proposed
model is novel since it moreover incorporates
boundaries for example quiet transporters,
friendliness of recently contaminated individual
and unregistered kicked the bucket Covid tainted
individuals alongside the disease rate, suspected
rate and demise rate. These boundaries contribute a
great deal to sort out the clearer model, alongside
fundamental boundaries. The model takes the
testing pace of suspected individuals into thought
and this rate differs as for period of the scourge
development. Proposed 3P-SIRD model is
partitioned into three-stages dependent on the
mindfulness and supportability of illness. Time is
separated into various periods as pace of disease
and recuperation vacillates district to area. The
model is tried on China information and is
sufficiently productive to propose a model near
their real figures of contaminated individuals,
recuperated individuals, kicked the bucket and
dynamic cases. The model predicts the ideal
lockdown time frame as 73 days for China which is
near their genuine lockdown period (77 days).
Further, the model is carried out to foresee the
ideal lockdown time of India and Italy.
(SoniyaLalwani, GunjanSahni, BhawnaMewara,
Rajesh Kumar; 2020)
In this paper, we research the continuous elements
of COVID-19 in India after its rise in Wuhan,
China in December 2019. We examine the impact
of cross country lockdown executed in India on
March 25, 2020 to forestall the spread of COVID-
19. Vulnerable Exposed-Infectious-Recovered
(SEIR) model is utilized to gauge dynamic
COVID-19 cases in India thinking about the impact
of cross country lockdown and conceivable
expansion in the dynamic cases after its expulsion
on May 3, 2020. Our model predicts that with the
continuous lockdown, the pinnacle of dynamic
contaminated cases around 43,000 will happen in
the mid of May, 2020. We likewise anticipate a 7
to 21% increment in the pinnacle worth of dynamic
tainted cases for an assortment of speculative

situations mirroring a general unwinding in the
control systems carried out by the public authority
in the post-lockdown time frame. For India, it is a
significant choice to think of a non-drug control
procedure, for example, cross country lockdown
for 40 days to delay the higher periods of COVID-
19 and to stay away from serious burden on its
general medical services framework. As the
continuous COVID-19 flare-up stays a worldwide
danger, it is a test for every one of the nations to
concoct compelling general wellbeing and
regulatory techniques to fight against COVID-19
and support their economies. (ChintamaniPai,
AnkushBhaskar, VaibhavRawoot)
III. PROBLEM IDENTIFICATION
The recognized issue in existing work is according to
the accompanying:
The chances of identification of Covid 19 patients
may lack due to low precision.
Patients recovery is quite low due to obtaining
limited F1-Score and Accuracy.
IV. METHODOLOGY
1. The proposed prediction model SVM-GWO
(Support Vector Machine with Grey Wolf
Optimization) method consists of:
a) Create a new (N+1)-dimensional input dataset
(xT,c)T with N input features [xi,...,xN]T and one
output class c.
b) You may do this by multiplying the mean of each
feature fi by the standard deviation of each feature fi.
2. Implementation of Interactive Computer Aided
Design
Apply the ICA algorithm to the new dataset, and save
the weight matrix W of dimension (N+1) (N+1).
3. Shrinkage of Small Weights
A. Calculate the absolute mean for each N+1
independent row vector Wi of W.
B. In case |wij| is less than or equal to ai, decrease
|wij| to zero. As you can see from the above, is a
tiny positive number.
4. Extraction of candidate features
A. Create an N-dimensional row weight vector W'i
for each weight vector Wi by projecting it over
the original input feature space (i.e., deleting
weights wi,N+1) that correspond to the output
class).
B. Create a (N+1)-dimensional vector bymultiplying
new weight matrix W' of dimension (N+1) N by
the original input data x. The components fi's of
this vector are new feature possibilities.
5. Removing unsuitable features
A. Formulate F = W'i x 1 ••• N+1 as a list of feature
candidates.
Set FS to F.
B. When a feature candidate fi's weight for class wic
is 0, then it should be excluded from FS;
C. For each feature candidate fi, if corresponding
weights wij = 0 for all j ∈ 1 ··· N, then exclude fi
from FS.
D. It also incorporates final N' extracted features in
its FS output.
6. Calculate a decision function using the following
parameters as predictors.
Fs = Number of vectors
Nsv = Number of Support Vectors
Nft = Number of features in support vector
SV[Nsv] = Support Vector Array
IN[Fs] = Input Vector Array
F = Decision Function Array
for 1
i ← to Fs by 1 do
F = 0
for 1
j ← to Nsv by 1 do
dist = 0
for 1
k ← to Nft by 1 do
dist += (SV[j].feature[k] – IN[i].feature[k])2
end
( )
*
exp
[j].
dist
F SV
κ γ
α κ
= − ×
+ = ×
end
F = F + b*
end
V. RESULTS AND ANALYSIS
The following observations are collect during process
of proposed model on patient dataset. Accuracy,
Precision and F1-Score parameters are calculate as
follows:
Table 1: Estimation of Accuracy in between of
SVM and Proposed Prediction Model
Import Data SVM SVM-GWO (Proposed)
200 0.4 0.51
400 0.53 0.57
600 0.49 0.53
800 0.57 0.61
1000 0.51 0.55

Figure 1: Graphical Analysis of Accuracy in
between of SVM and Proposed Prediction Model
The above graph show that the proposed model gives
better prediction accuracy as compare than SVM.
When sample data size is 200 then accuracy improve
by 27.5%. In a similar way, when sample data is 1000
then accuracy improve by 7.8%.
Table 2: Estimation of Precision in between of
200 0.41 0.48
400 0.52 0.56
600 0.48 0.52
800 0.56 0.6
1000 0.5 0.54
Figure 2: Graphical Analysis of Precision in
between of SVM and Proposed Prediction Model
better prediction precision as compare than SVM.
When sample data size is 200 then precision improve
by 17%. In a similar way, when sample data is 1000
then accuracy precision by 8%.
Table 3: Estimation of F1-Score in between of
200 0.4 0.46
400 0.57 0.59
600 0.53 0.56
800 0.58 0.61
1000 0.52 0.53
Figure 3: Graphical Analysis of F1-Score in
between of SVM and Proposed Prediction Mode.
better prediction F1 score as compare than SVM.
When sample data size is 200 then F1 score improve
by 15%. In a similar way, when sample data is 1000
then F1 score improve by 2%.
VI. CONCLUSIONS
The proposed model gives preferred forecast
exactness as analyze over SVM. At the point when
test information is 1000 then exactness improve by
7.8%.
The proposed model gives preferable forecast
accuracy as look at over SVM. At the point when test
information is 1000 then exactness accuracy by 8%.
The proposed model gives better forecast F1 score as
think about than SVM. At the point when test
information is 1000 then F1 score improve by
2%.Consequently, characterization of patients
according to Covid-19 illness indications are better
arranged through proposed strategy SVM-GWO
(Support Vector Machine with Gray Wolf
Optimization).
Our proposed philosophy assists with working on the
exactness of analysis and enormously accommodating
for additional treatment. In future improvements, the
exactness must be tried with various dataset and to
apply other AI calculations to check the precision
assessment. The impediment of the proposed model is
handling time, on account of tremendous measure of
information taken for assessing the exhibition of train
information. In future, similar calculations to be
carried out with continuous information for assessing
the adequacy of the framework.
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Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid 19 Pandemic Crisis

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Health Risk Prediction Using Support Vector Machine with Gray Wolf Optimization in Covid 19 Pandemic Crisis