Machine Learning Presentation

1. Machine Learning-
Based Model to
Predict the Disease
Severity and
Outcome inCovid-19
Patients
Presented By :
AYANA RUKASAR

2. Agenda
Introduction
Problem Statement
Motivation
Objective
Work Progress
WorkTo Be Done
Literature Survey
Research Gap
Dataset Used
Methodology
Conclusion
References

3. Introduction
 Coronaviruses are a large family of viruses that are
known to cause illness ranging from the common
cold to more severe diseases such as Middle East
Respiratory Syndrome (MERS) and Severe Acute
Respiratory Syndrome (SARS). A novel coronavirus
(COVID-19) was identified in 2019 in Wuhan, China.
 A pandemic disease, COVID-19, has caused trouble
worldwide by infecting millions of people. The studies
that apply artificial intelligence (AI) and machine learning
(ML) methods for various purposes against the COVID-19
outbreak have increased because of their significant
advantages. Although AI/ML applications provide
satisfactory solutions to COVID-19 disease, these
solutions can have a wide diversity. This increase in the
number of AI/ML studies and diversity in solutions can
confuse deciding which AI/ML technique is suitable for
which COVID-19 purposes.

4. Problem
Statement
Its 25th MarchAfternoon and India has reported its 9th death
with 562 total confirmed cases due to COVID-19. Fresh cases
from Manipur, Bihar, Gujrat, and Madhya Pradesh have been
reported by the Union Ministry of Health and FamilyWelfare . As
the coronavirus outbreak continues to spread in the country, the
question that we as Indians are trying to answer is:
Its 25th MarchAfternoon and India has reported its 9th death
with 562 total confirmed cases due to COVID-19. Fresh cases
from Manipur, Bihar, Gujrat, and Madhya Pradesh have been
reported by the Union Ministry of Health and FamilyWelfare . As
the coronavirus outbreak continues to spread in the country, the
question that we as Indians are trying to answer is:
"Will India be able to tackle this pandemic or are we going to
witness another Italy/ S.Korea/Wuhan?"
e able to tackle this pandemic or are we going to witness
another Italy/ S.Korea/Wuhan?"

5. Motivation
 . The current COVID-19 pandemic is showing
negative effects on human health as well as on
social and economic life.

6. Objective of the Research
 We need a strong model that predicts how the virus could spread across different
countries and regions.The goal of this task is to build a model that predicts the
spread of the virus in the next 7 days.

7. Work Progress
Literature Survey has been
done.
Different machine
learning models has
been applied and their
respective accuracy has
been calculated.

8. Work to be
done
Drafting of review article
We will analyse the outbreak of coronavirus
across various regions, visualize them using
charts and graphs and predict the number of
upcoming cases for the next 10 days using
Linear Regression and SVM model in Python.

9. Literature
Survey
 The literature survey of 5 research papers from the
journal of national and international importance has
been done so far and are given below.

10. Literature
Survey
Authors
Journal Outbreak Infection Machine Learning
Ruirui
Liang
Transboundary and
Emerging Diseases
Swine fever Random Forest
R Gupta Infection Disease
Modelling
Dengue Classification &
Regression tree
(CART)
Sina F.
Ardabili
Infectious Disease susceptible–infected–
recovered (SIR) and
susceptible-exposed-
infectious-removed
(SEIR) models
Genetic algorithm
(GA).

11. LiteratureSurvey
Authors Journal Outbreak
Infection
Machine Learning
Abbas A, Classification of COVID-19 chest X-ray images deep convolutional neural
network
Sethy PK Detection of coronavirus
disease (COVID-19)
diagnosis support vector machine

12. ResearchGap
There are three major subfields that can be
considered as future works.
Improvement of the
mechanism used for
collection and
management of datasets.
Finding suitable algorithms
for analysing the collected
data .
Investigating each major
coronary artery,separately.
Various ML methods and data processing techniques
have been proposed for Machine Learning-Based
Model to Predict the Disease Severity and Outcome in
Covid-19 Patients, there are few avenues for
improvement that require deep attention.

13. DatasetUsed
Italy dataset has been used and this database contains
188 attributes out of which a subset of 14 of them has
been used.
 Country/Region
 Confirmed
 Deaths
 Recovered
 Active
 New cases
 New Deaths
 New Recovered

14. Methodology
Different machine learning models has been applied and
their respective accuracy has been calculated.
Random Forest
SupportVector Machine
Classification And Regression trees
Genetic Algorithm
ANN (Artificial Neutral Network)

15. Random Forest
Algorithm
 Random Forest is a popular machine learning algorithm
that belongs to the supervised learning technique. It can
be used for both Classification and Regression problems in
ML. It is based on the concept of ensemble
learning, which is a process of combining multiple
classifiers to solve a complex problem and to improve the
performance of the model.
 As the name suggests, “It is a classifier that contains a
number of decision trees on various subsets of the
given dataset and takes the average to improve the
predictive accuracy of that dataset." Instead of relying
on one decision tree, the random forest takes the
prediction from each tree and based on the majority votes
of predictions, and it predicts the final output.
 The greater number of trees in the forest leads to higher
accuracy and prevents the problem of overfitting

17. SupportVector Machine
 SupportVector Machine or SVM is one of the most popular supervised learning
algorithms, which is used for classification as well as regression problems.
However, mainly, it is used for classification problems.
 The goal of the SVM algorithm is to create the best line or decision boundary that
can segregate n-dimensional space into classes so that we can easily put the new
data point in the correct category in the future.This best decision boundary is
called a hyperplane.

19. ANN (Artificial
Neutral
Network)
Artificial neural network (ANN) is a machine learning approach that
models human brain and consists of a number of artificial neurons.
Neuron in ANNs tends to have fewer connections than biological
neurons.
Each neuron in ANN receives a number of inputs.
An activation function is applied to these inputs which results in
activation level of neuron (output value of the neuron).
Knowledge about the learning task is given in the form of examples
called training examples.

20. The ANN elements can be defined as:
Input Layer: It consists of neurons which represents features or variables for
problem solving.
Output Layer: It consists of neurons which represents result of calculation in cell
body.
Weights: It is strength of connection between the neurons.
Activation function: It is function to obtain output according problem which is to
be solved.
Learning Function: It is also called optimization function. It is used to obtain
minimum error by updating weights.
Hidden layer: It is optional & it depends on ANN architecture.

21. 21
Fig.: Basic Representation Artificial Neural
Network(ANN) [11]

22. Genetic
Algorithm
 The genetic algorithm is a method for solving
both constrained and unconstrained
optimization problems that is based on natural
selection, the process that drives biological
evolution. The genetic algorithm repeatedly
modifies a population of individual solutions.

24. Classification &
Regression
Trees
 Decision Trees are an important type of algorithm for
predictive modeling machine learning.
 The classical decision tree algorithms have been around for
decades and modern variations like random forest are among
the most powerful techniques available.
 In this post you will discover the humble decision tree
algorithm known by it’s more modern name CART which
stands for Classification And Regression Trees. After reading
this post, you will know:
• The many names used to describe the CART algorithm for
machine learning.
• The representation used by learned CART models that is
actually stored on disk.
• How a CART model can be learned from training data.
• How a learned CART model can be used to make predictions
on unseen data.
• Additional resources that you can use to learn more about
CART and related algorithms.

26. Tasks to be
Performed
Analysing the present condition in India
Is this trend similar to Italy/S. Korea/ Wuhan
Exploring the world wide data
Forecasting the world wide COVID-19 cases
using Prophet

27. Conclusion
COVID-19 is still an unclear infectious disease, which
means we can only obtain an accurate SEIR prediction
after the outbreak ends.The outbreak spreads are
largely influenced by each country’s policy and social
responsibility.As data transparency is crucial inside the
government, it is also our responsibility not to spread
unverified news and to remain calm in this situation.
The Machine Learning-Based Model to Predict the
Disease Severity and Outcome in Covid-19 Patients
project has shown the importance of information
dissemination that can help in improving response
time, and help planning in advance to help reduce risk.
Further studies need to be done to help contain the
outbreak as soon as possible.

28. References
 https://www.javatpoint.com/machine-learning-random-forest-
algorithm
 https://www.javatpoint.com/machine-learning-classification-
regression-tree-classification-algorithm
 https://www.javatpoint.com/support-vector-machine-algorithm-
for-machine-learning
 https://www.javatpoint.com/artificial-neural-network

29. THANKYOU