A COMPREHENSIVE SURVEY ON CARDIAC ARREST RISK LEVEL PREDICTION SYSTEM

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 2549
A COMPREHENSIVE SURVEY ON CARDIAC ARREST RISK LEVEL
PREDICTION SYSTEM
Priyanka K M 1, Priyanka B G 2, R Tejonidhii 3, Raksha S Hegde 4, R Kasturi Rangan 5
1-4 Students,Information Science and Engineering
5 Professor, Dept. of Information Science and Engineering
Vidyavardhaka College of Engineering, Mysuru, Karnataka, India
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Abstract - These days, Heart related problems are
increasing day by day because of way of life, inherited. Most
importantly coronary illness is the new normal, hence the
lifespan of them is in greater danger. So, identification of
Cardiac disease at early stages and providing proper
treatments plays a vital role in the current medical sector.
Many efficient techniques and algorithms are utilized for the
disease prediction with interest in Cardiac sector. As per
medically demonstrated results the ordinary Bloodpressureis
120/90 and heart beat rate is 72bpm. Thepatientdangerlevel
is grouped utilizing machine learning and data mining
arrangement procedures like Naive Bayes, Support Vector
Machine, KNN, Logistic Regression, Decision Tree, Neural
networks, Random Forest (RF).
Key Words: Cardiac disease, DataMining, Naive Bayes, KNN,
SVM, Logistic Regression, Decision Tree, Neural networks,
Random Forest.
1. INTRODUCTION
Nowadays, Heart related issues are growing day by day in
view of lifestyle, acquired. Especially, coronary ailment has
become more ordinary these days which makes people life
difficult. The patient risk level is calculated using SVM,
Logistic Regression, KNN and many more. A few non-
modifiable variables are additionally present such as
smoking, drinking additionally justificationbehindcoronary
illness. Whenever blood veins are overstretched, the danger
meter of the veins are expanded which prompts the blood
pressure.
Age is a factor that is non-modifiable and is a danger which
additionally one of the justifications for coronary illness.
Nearly more than 41% of demise due to heart sickness can
be seen due to the smoking as the major issue. Since it
majorly cut-offs the entry of oxygen to the blood thereby
causing harm to the veins. Different mining strategies are
used to foresee the risk of cardiac illness. The patient
movement is observed carefully and often in the event that
there are any progressions happen, the hazard level of
illness is said to the patient by specialist
2. Generic Block Diagram
Fig 1: Flowchart for prediction
In the initial phase, we gather clinical information. In earlier
years patients' information was gathered for handling.More
than 5000 + informational collections are utilized for
handling. Preparing informational indexes will contain
patient subtleties and furthermore boundaries that are
expected for expectation. Clinical information is dissected
and only significant information is extricated. Required
information extraction is done in light of the fact that whole
preparation information is not needed for handling and
assuming we input all information, it requires a lot of time
for handling, so information handling is finished.Supervised
Learning Technique: It's a prescient model utilized for the
assignments where it includes a forecast of one worth
involving different qualities in the informational index. We

have numerous calculationstoconstructmodelsinregulated
learning like KNN, Naive Bayes, and so forth. Depending on
the necessity, marks, boundaries, and informational index
we select the fitting calculation for expectations.
The calculation is utilized to construct a model that makes
forecasts in light of proof within the sight of vulnerability.
The doctor can access the module where the framework
predicts the cardiac problems and types for the new
patients. Results produced by the algorithm are checked
with precision by utilizing the confusion matrix strategy,
here we approve the outcomes produced by the calculation.
The final results are displayed on GUI. At the point when
clients get to sign in to the application, the framework
predicts the sickness and presentations it on a GUI.
3. Challenges
There are several challenges in each stage of cardiac disease
prediction:
1. Choosing the right dataset: Plenty of datasets are
available of different varieties. Picking therightone
which serves our purpose is crucial as the whole
process depends on this phase.
2. Picking the right attribute for producing the
accurate outcome: We may have 100+ fields in our
data. But eliminating the fields whose absence
doesn't affect the performance of the algorithm is
important.
3. Distinguishing and takingcareofthemissingvalues.
In information pre-processing, it is crucial to
distinguish and accurately handlethemissingparts,
neglecting to do this, you couldmakeerroneousand
flawed determinations and inductions from the
information. Obviously, this will hamper the
venture.
4. Choosing the suitable algorithm to achieve a good
accuracy score: When it comes to algorithms it isall
about accuracy, sopickingtherightalgorithmwhich
yields good results is a must.
5. Assess the model's presentation and lay out
benchmarks. Assessment incorporates model
metric assessment, confusion matrix estimations,
KPIs, model executionmeasurements,model quality
estimations and a last assurance of whether the
model can meet the laid-out business objectives.
6. Repeat and change the model: Even however the
model is functional and you're constantly checking
its exhibition, you're not done. With regards to
carrying out advances, it's generally expected said
that the recipe for progress istobeginlittle,imagine
something truly mind-blowing and repeat
frequently.
4. Motivation:
More number of individuals are getting coronary disease
consistently and these are the greatest enemy of people all
over the planet. The World Health Organization (WHO)
examined that twelve million occurring happens overall
because of Heart illnesses. Coronaryillnessistakingthelives
of many individuals in a very frequent manner Clinical
conclusion assumes an essential part but is a muddled task
that should be executed proficiently and precisely. To
decrease the cost of clinical tests, suitable PC-based data is
required. Learning of the major risky parts associated with
coronary disease helps administrations specialists to
perceive patients at high risk of having cardiac illness.
Finally for the purpose of human living benefits, spreading
awareness to the society and helping them is the main
motivation.
5. Literature Review
Different studies have been done and models has built for
prediction of heart disease,various data miningandmachine
learning methods are used for predicting the disease and
accuracy level is obtained for various methods
In [1] Theresa Princy, et al. The risk level for the patient is
classified using machine learning techniques like KNN and
ID3 algorithm. There are two phases namely Classification
and regression, study consists of two modules i.e., classifier
and prediction module. Using basic attribute such as BP,
Cholesterol the accuracy level ofthepredictionwas40%and
by adding smoking and family history the accuracy was
increased to 80%. In [10] C. Kalaiselvi et al. the main
objective is to diagnose heart disease with the reduced
number. Classification and clustering algorithm are used in
this method. The implementation part is done using the
MATLAB 12 where data is classified as two types as getting
heart disease and with no heart disease. The cost is reduced
for different medical test and it help patients to take
precautionary measures in advance. The accuracy for naive
bayes classifier is about 90.72%, decision tree is of 96.62%
and KNN is about 97%.
In [6] N. Komal Kumar et al. mainly determines about the
prediction the ailment dependent on the side effects and
conditions of patient and mainly deals with cardio vascular
disease. The cleaned data is split into two types namely
training and testing data and subjected to 5 classifiers and
accuracy is found by confusion matrix thereby finding best
classifier for predicting cardiovascular disease. Among all
the predicted models, the higher accuracy was observed
using random forest with 85.71%. The other models like
decision tree and logistic regression have accuracy of
74.28%, SVM with 77.14% and KNN with 68.57%.
Mortality of the patients with heart disease is predicted
using data mining methods [4]. ANN [Artificial neural

networks], logistic regression anddecisiontreearethethree
models used in predicting death of patients within 12
months of suffering from disease. In this model, RapidMiner
studio was used for implementation. Model performance is
dealt with some of the criteria like accuracy, specification
and precision Among the three methods, neural network
gives the higher accuracy of 74.3%. In [2] Sana Bharti, Dr.
Saliendra Narayana Singh talks about how various
algorithms can be used to predict the heart disease. The
algorithms include data mining techniques like clustering,
association rule for prediction. The basic flow of this model
consists of database of patients, analysis of data, feature
selection, optimization algorithm,training andclassification.
There are total of 14 attributes used in this method like age,
gender, BP level, obesity, heart rate, sugarlevel etc.Thistype
of prediction has an accuracy rate of 60.74%.
The [5] is about prediction of cardiac disease by using
decision tree method with an accuracy rate of 73% where as
other major algorithm like Naïve Bayes, KNN [K-Nearest
neighbors], SVM, Logistic Regression which has a range of
accuracy rate about 60-70%. The objective of thispaperisto
use a cardiac dataset which is grouped by using supervised
machine learning algorithms. Some method to find out the
accuracy of an algorithm include Accuracy prediction,
precision, this method has proven more accuracy using
decision tree. In [12] it is a study conducted by different
algorithms like Naive Bayes, KNN, Logistic Regression.
Regularization of model is used in thisapproachwhichhelps
in improving overall accuracy rate. Due to complexnature of
cardiac disease, the models need professional advices,
implementation of patient’s issues to determine how severe
is the attack on heart and also any previous attack infoof the
patient. Logistic regression has an accuracy of 77% and
81.48%
Accuracy with naïve bayes, highest accuracy is recorded in
KNN model with 94.60%.
In [3] Seyedamin Puriyed et al. have provided aim to
compare different machine learning techniques on dataset.
The algorithms are applied individuallyandincombinations
using precision(sensitivity). In [9] Khashman adan, et al.
mainly determine about the discussion regarding the death
rate infused by various cardiovascular heart disease, by
designing an intelligent system for preventing misdiagnosis
of heart disease. The dataset is divided into two categories
namely train and test set. The dataset is in theratioformatof
60:40. The advantage of this type of division is to avoid
underfitting of the data which occur if the training dataset is
very smaller than the testing dataset. The designing of
network includes standard ratio of dividing datasets.Neural
network at once is provided for batch training's entire
patterns. The accuracy was more when back propagation
method is used with neural network (BPNN), gives accuracy
of 85%.
In [8] Sanjida Reza Rafa et al. Principal Component Analysis
(PCA) is used to reduce the count of attributes. K-means
method is used for clustering the data. Thedimensionality of
dataset is reduced by PCA and then combined them with
heuristic algorithm for better optimization, the accuracy for
decision tree is about 99.62%.
A portion of the regulated AI strategies[11] utilized in the
expectation of coronary illness are artificial neural network
(ANN) has model with human like neurons, decision tree
(DT) with classification and regression tree, random forest
(RF), support vector machine (SVM) with one, two, three,
four dimensions as point, line, plane and hyperplane
respectively, naïve Bayes (NB) with independency of the
attributes and k nearest neighbors algorithm .It also
mentions about various kind of heart diseases with respect
to different attributes that are considered. Grid search and
random search algorithms are used for selecting features
and hence provides better results. Decision tree has 86%
accuracy during testing phase and 87% during training
phase. Saba Bashir et al. predicts theheartdiseaseusingdata
science [7]. The research is based on feature selection
methods and algorithms. The main objective istoprovidean
accurate way of diagnosing disease with reducedfeatureset.
The results obtained shows accuracy of 84.85% by using
Logistic regression (SVM) and 84.17% for Random Forest
classifier.
In [13] the model purposes AI for foreseeing the gamble of
coronary failure, as ML can anticipate precisely and simply
decide. The proposedframework utilizesClevelandcoronary
illness dataset and information mining techniques like
regression and classification and AI strategies like Random
Forest, Decision tree, hybrid breed model is utilized. The
exactness pace of Decision tree is 79%, for random forest is
around 81%, when hybrid of these both are taken the
precision is more with 88%.
6. Dataset Description
The dataset has 3 sorts of characteristics specifically key,
information and forecast attributes [1]. Out of many info
credits, age and orientation are nonmodifiable qualities.
Patient information gathered (2011 to 2017) at
cardiovascular ward ofMostarmedical clinicwereutilizedto
fabricate the model [4], the dataset contains 507 patients
with cardiovascular infection, it has 71 characteristics and
two specials including patient ID, Vital status.Differenttraits
are Peripheral Oedema, HbA1c, ACE inhibitors and so forth.
In [12] the Cleveland coronary illness dataset fromUCIvault
is utilized; the dataset has 303 cases with 76 characteristics
for certain inadequate properties. Six examples were
dismissed in the concentrate because of missed esteem in
task sections and hence total of 279 tests. In [2] the dataset
has 14 ascribes for coronary illness forecast resting
electrocardiographic outcomes, resting pulse, practice
incited angina is few many among them.

Table -1: Literature review of survey papers
Sl. No Citations Year of
Publication
Paper type Dataset Result
1 Theresa Princy et al.
[1]
2016 Conference UCI machine Learning
data repository
Accuracy of prediction was at the
range of 80%
2 Sana Bharathi et al. [2] 2015 Conference Real time data Accuracy: 61%
3 Seyedamin Puriyed et
al. [3]
2017 Conference UCI dataset repository
and Cleveland dataset
KNN(K=15)->82.8%
Naive bayes->83.49%
Decision Tree->77.52%
SVM->69.96%
4 Damir Imamovic et al.
[4]
2020 Conference UCI machine Learning
Data Repository
Decision tree-> 65.13%
Neural networks-> 74.34%
Logistic regression->69.74%
5 Jane Preetha Princy et
al. [5]
2020 Conference UCI data repository and
Cardiovascular dataset
Decision tree-> 73%
Logistic Regression->72%
SVM->72%
KNN->60%
6 Sagarika et al. [6] 2020 Conference UCI Data Repository
Heart disease datasets
Random Forest-> 85.71 %,
7 Saba et al. [7] 2019 Conference UCI Repository Decision tree->82%
Logistic Regression->82.5%
Random Forest->84.2%
Naive Bayes->84%
Logistic Regression->85%
8 Sanjida et al. [8] 2020 Conference UCI Machine Learning
Repositoryheartdisease
dataset
Decision Tree (15 attributes)-
99.62%
Proposed system-94.06%.
9 Khashman et al. [9] 2015 Conference UCI machine Learning
Data Repository
Decision Tree->84.33%
Naïve Bayes->84%
BPNN->85%
10 Kalaiselvi et al. [10] 2015 Survey UCI machine Learning
Data Repository
NB->94.43%
DT->96.1%
KNN->96.5%
12 attributes used
NB->90.72%
DT->96.62%
KNN->97%
11 Polipireddy et al. [11] 2020 Survey Cleveland dataset UCI Chronic disease diagnosis
between 82% and 92%
12 Samir et al. [12] 2020 Conference UCI Machine Learning
dataset
Logistic regression->77%
Naïve Bayes->81.48
KNN->94.60%
SVM, MLP->80.41%
13 Sai et al. [13] 2021 Conference UCI Machine Learning
dataset
Decision Tree->79%
Random Forest->81%
Hybrid
(Decision Tree+RandomForest)-
> 88%

Table 2 Dataset sample of survey papers
7. CONCLUSIONS
As it is very hard to forecast the coronary illness in real time,
some efficient research works required to predict cardiac
disease in less time. Even though many works done on this
cardiac disease, still some efficient and proper research
works required to predictcardiac diseaseusingefficient data
science algorithms. Developing a real time systemalsohelps
in real time to predict cardiac disease in less time and better
way and doctors can handle the patients in better way
REFERENCES
[1] Thomas, J., & Princy, R. T. (2016, March). Human
heart disease prediction system using data mining
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[2] Bharti, S., & Singh, S. N. (2015, May). Analytical
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SL No Author Name No of attributes Dataset type
1 Theresa Princy et al. [1] 5 UCI Machine Learning Dataset
2 Sana Bharathi et al. [2] 9 Real Time Date
3 Seyedamin Puriyed et al. [3] 13 UCI Data with Cleveland dataset
4 Damir Imamovic et al. [4] 8 UCI Machine Learning Data repository
5 R. Jane Preetha Princy et al. [5] 12 UCI Cardiovascular Dataset
6 N. Komal Kumar et al. [6] 10 UCI heart disease dataset
7 Saba Bashir et al. [7] 15 UCI Machine Learning repository
8 Md. Touhidul Islam et al. [8] 15 UCI Machine Learning Dataset
9 Khashman adan [9] 270 instances; 13 attributes UCI Machine Learning Data repository
10 C.Kalaiselvi [10] 13 UCI Machine Learning Data repository
11 Rahul Katarya et al. [11] 13 UCI Data with Cleveland dataset
12 Samir S Yadav et al. [12] 297instances;13attributes Heart disease dataset
13 Dr. M. Kavitha et al. [13] 303instances;14attributes Heart disease dataset

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