Cardiovascular Risk Detection in SARS - CoV-2 suing ML

Cardiovascular Risk Detection in SARS-CoV-2
using Machine Learning
Authors: MD ASMA, NOVERA HABEEB ,
HEENA KHANUM
National Symposium on High Performance Computing Applications using AI for Bioinformatics and Bio
Medical Applications- July 14-15,2023
Presenter: MD ASMA, Associate Professor, CSE Dept
LORDS Institute of Engineering and Technology (A), Hyderabad, Telangana, INDIA.
In CSIR SPONSERED SYMPOSIUM
Organized by
International School of Technology and Sciences for Women

Medical Applications July 14-15,2023
Presenter Name : MD ASMA
Qualification : B.Tech- JNTUH, M.Tech-JNTUH,
Pursuing Ph.D
Teaching Experience : 16yrs
Publications : 4
BIOGRAPHY

ABSTRACT
• Almost 17.9 million people are losing their lives due to
CardioVascular Disease, which have increased after the COVID-19
pandemic to 32% of total death throughout the world.
• Technologies like MCGs help in detecting these diseases in early
stage, but expensive, unfit for smaller clinics, time-consuming and
sensitive to tangential causes
• With AI entering and Machine Learning models, This study is
aimed at building a potential machine learning model to predict
heart disease in early stage .

INTRODUCTION
Cardiovascular disease (CVD) is a
general term that describes a
disease of the heart or blood
vessels.
Blood flow to the heart, brain or
body can be reduced because of a:
•blood clot (thrombosis)
•build-up of fatty deposits inside an
artery, leading to the artery
hardening and narrowing
(atherosclerosis)

Types of CVD

•Pre-existing cardiovascular disease (CVD) increases the morbidity
and mortality of COVID-19 and is strongly associated with poor
disease outcomes.
•However, SARS-CoV-2 infection can also trigger acute and
chronic cardiovascular disease.
Acute cardiac complications include arrhythmia, myocarditis
and heart failure, which are significantly associated with higher
in-hospital mortality.
The possible mechanisms by which SARS-CoV-2 causes this
acute cardiac disease include direct damage caused by viral
invasion of cardiomyocytes as well as indirect damage through
systemic inflammation.

•The Multifunction Cardiogram (MCG) is a kind of non-
invasive diagnostic tool for measuring the health of a
patient’s heart
•The MCG uses a systems approach to modeling the heart
and then comparing these models to a demographically
appropriate risk-stratified database of similar patients using
166 indices with that database to aid both in the diagnosis
and in predicting patient needs and outcomes.
•Expensive, unfit for smaller clinics, time-consuming and
sensitive to tangential causes

•Researchers are employing artificial intelligence (AI) in an effort to mine new
medical information that can be used by clinicians to better understand the
symptoms of various diseases and, as a result, make more informed decisions
for patients
•The use of artificial intelligence (AI) and massive amounts of data in the
prediction of CVD models is becoming increasingly common.
•In view of the growing popularity of machine learning techniques, The
traditional machine learning models that were tested and evaluated based on
UCI Heart Disease dataset is used to having 14 columns and over 300
samples..

METHODOLOGY
Classification methods:
Models such as Random forest (RF), decision tree classifier (DT), K-
Nearest Neighbors(KNN), Support Vector Machine (SVM) are
used

Implementation
Dataset: 300 entries of UCI Heart Disease dataset with 14
columns(data likeage,sex,cp,trestbp,chol,fbs)
Implementation platform: Google collaboratory using PYTHON
Packages used: Numpy
Pandas
Sci-kit

EXPERIMENTAL ANALYSIS
 The experiments focused on the performance of KNN using the datasets
and features.
 As the name says, a k neighbors classifier takes a data point and
finds k other data points nearest to it in the vector space. In a
supervised fashion, KNN creates clusters of the data samples having
the same target value. Whenever a new value needs to be classified,
it uses a distance metric to assign it to one of the classes. For heart
disease detection, there are only two classes that KNN needs to build
 For the first set of results the training dataset was divided at random into
five folds, with training on four of the five folds, and testing on the
remaining fold.
 Models were trained on data set and achieved accuracy 91.5% with KNN
Model.

 List the dataset Perform EDA

 Train the data on CP data values
 The limited subset of the attributes we encoded and scaled. For
instance, the chest pain data variable expanded
into cp_0, cp_1, cp_2, and cp_3. This normalized and engineered
dataset will be appropriate for training.

 Using the KNeighborsClassifier module we build a KNN
model and iteratively tune the
hyperparameter n_neighbors (number of neighbours to
be checked for every data point).
 Models were trained on data set and achieved accuracy 91.5%
with KNN Model.

 The experiments focused on the performance of KNN using the
datasets and features
 Models were trained on data set and achieved accuracy 91.5% with
KNN Model.
Algorithm Accuracy
Random Forest 86.8%
KNN 91.8%
Decision Tree 85.2%
SVM 90.1%

CONCLUSION
To conclude, Early identification of heart disease of improved
diagnosis and high-risk individuals using a prediction model can
be recommended for a fatality rate reduction, and decision-
making is improved for further treatment and prevention.

Thankyou…!!

Cardiovascular Risk Detection in SARS - CoV-2 suing ML

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