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1. MULTIPLE DISEASE PREDICTION USING
MACHINE LEARNING
Guide:
 MR.M.BALA SUBRAMANIAN,
Associate Professor/CSE
GROUP MEMBERS:
 ARUN S (111919104011)
 VIGNESH P (111919104159)
 DINESH KUMAR D (111919104031)

2. ABSTRACT:
Disease Prediction using Machine Learning is the system that is
used to predict the diseases from the symptoms which are given
by the patients or any user. The system processes the symptoms
provided by the user as input and gives the output as the
probability of the disease. Decision Tree classifier is used in the
prediction of the disease which is a supervised machine learning
algorithm. The probability of the disease is calculated by the
Decision Tree algorithm. With an increase in biomedical and
healthcare data, accurate analysis of medical data benefits early
disease detection and patient care. By using linear regression and
decision tree we are predicting diseases like Diabetes, Malaria,
Jaundice, Dengue, and Tuberculosis.

3. LITERATURE SURVEY:
S.NO TITLE DESCRIPTION PROS CONS
1. Artificial neural
networks in image
processing for
earlier detection.
An Artificial neural
network contains
layersof interconnect
hubs or neurons.A
neuron in artificial
neural network is a
function that
accumulates the data
and classifies the
data as per a
particular pattern.
It provides us
with more
reliable bases
for making
decision and
improves our
ability to
anticipate
various
outcomes in
considering.
Perceptrons can
only categorize
sets of vectors
that can be
separated linearly.

4. S.NO TITLE DESCRIPTION PROS CONS
2. The utilisation
of machine
learning
approaches for
medical data
classification
Machine learning in
healthcare can be
used to develop better
diagnostic tools to
analyze medical
images. For example, a
machine learning
algorithm can be used
in medical imaging
(such as X-rays or MRI
scans) using pattern
recognition to look for
patterns that indicate a
particular disease.
Trends and
Patterns Are
Identified With
Ease.
There's a High
Level of Error
Susceptibility.

5. S.NO TITLE DESCRIPTION PROS CONS
3. Biomarkers in
diabetic
retinopathy.
The absence or presence,
type, and severity of
retinal vessel lesions
diagnose by
ophthalmoscopy or by
my driatic or non-
mydriatic retinal
photography are
biomarkers of diabetic
retinopathy status. These
markers are used in
routine clinical practice
and in research.
Diabetic
retinopathy is
best diagnosed
with comprehe
nsive dilated
eye exam.
It will not more
accurate.

6. S.NO TITLE DESCRIPTION PROS CONS
4. International clinic
diabetic retinopathy
disease severity
scale detailed table.
Based on these
findings, the authors
developed a Diabetic
Retinopathy
Severity Scale
(DRSS) that divides
DR into 13 levels
ranging from
absence of
retinopathy to severe
retinopathy
including vitreous.
It provides us
with more
reliable bases
for making
decisions and
improves our
ability to
anticipate
various
outcomes in
considering the
available data.
Perceptrons can
only categorize
sets of vectors
that can be
separated
linearly.

7. S.NO TITLE DESCRIPTION PROS CONS
5. Improved study
of heart disease
prediction system
using data
mining
classification
technic use
The overall objective of
our work is to predict
more accurately the
presence of heart
disease. In this paper,
two more input attributes
obesity and smoking are
used to get more
accurate results.
Trends and
Patterns Are
Identified With
Ease.
There's a High
Level of Error
Susceptibility.
6. A naval
encryption for
end to end secure
fiberoptic
communication
End-to-end encryption
is a security method that
keeps your
communications secure.
End-to-end
encryption has
some obvious
advantages over
"clear text" (when
messages or data
are sent without
any encryption at
all) andencryption-
in-transit.
But end-to-end
encryption isn't
the perfect
solution to every
kind of
communication
need.

8. EXISTING SYSTEM:
• The traditional diagnosis approach entails a patient visiting a
doctor, undergoing many medical tests, and then reaching a
consensus.
• This process is very time-consuming. This project proposes an
automated disease prediction system to save time required for
the initial process of disease prediction that relies on user input.
• The user gives input to the system and system provides the
user with a set of probable diseases.

9. PROPOSED SYSTEM:
• In multiple disease prediction, it is possible to predict more
than one disease at a time. So the user doesn’t need to traverse
different sites in order to predict the diseases.
• We are taking three diseases that are Liver, Diabetes, and
Heart. As all the three diseases are correlated to each other. To
implement multiple disease analyses we are going to use
machine learning algorithms and Flask.
• When the user is accessing this API, the user has to send the
parameters of the disease along with the disease name. Flask
will invoke the corresponding model and returns the status of
the patient.

10. S/W & H/W REQUIREMENTS:
HARDWARE:
• CORE i5
• 8GB RAM
SOFTWARE:
• HTML (VERSION 5)
• CSS
• JAVASCRIPT
BACKEND:
• PYTHON (Flask)
• KAGGLE (Data Set)
• Google Colab

11. MODULES:
1. Collecting Data
2. Preparing the Data
3. Choosing a Model
4. Training the Model
5. Evaluating the Model
6. Parameter Tuning
7. Making Predictions

12. Modules description:
1. Collecting Data:
• As you know, machines initially learn from the data that you
give them. It is of the utmost importance to collect reliable
data so that your machine learning model can find the correct
patterns. The quality of the data that you feed to the machine
will determine how accurate your model is. If you have
incorrect or outdated data, you will have wrong outcomes or
predictions which are not relevant.
• Make sure you use data from a reliable source, as it will
directly affect the outcome of your model. Good data is
relevant, contains very few missing and repeated values, and
has a good representation of the various subcategories/classes
present.

13. 2. Preparing the Data:
• Putting together all the data you have and randomizing it. This
helps make sure that data is evenly distributed, and the
ordering does not affect the learning process.
• Cleaning the data to remove unwanted data, missing values,
rows, and columns, duplicate values, data type conversion, etc.
You might even have to restructure the dataset and change the
rows and columns or index of rows and columns.
• Visualize the data to understand how it is structured and
understand the relationship between various variables and
classes present.
• Splitting the cleaned data into two sets - a training set and a
testing set. The training set is the set your model learns from.
A testing set is used to check the accuracy of your model after
training.

14. 3. Choosing a Model:
A machine learning model determines the output you get after
running a machine learning algorithm on the collected data. It is
important to choose a model which is relevant to the task at hand.
Over the years, scientists and engineers developed various
models suited for different tasks like speech recognition, image
recognition, prediction, etc. Apart from this, you also have to see
if your model is suited for numerical or categorical data and
choose accordingly.

15. 4. Training the Model:
• Training is the most important step in machine learning. In
training, you pass the prepared data to your machine learning
model to find patterns and make predictions. It results in the
model learning from the data so that it can accomplish the task
set. Over time, with training, the model gets better at
predicting.

16. 5. Evaluating the Model:
• After training your model, you have to check to see how it’s
performing. This is done by testing the performance of the
model on previously unseen data. The unseen data used is the
testing set that you split our data into earlier. If testing was
done on the same data which is used for training, you will not
get an accurate measure, as the model is already used to the
data, and finds the same patterns in it, as it previously did. This
will give you disproportionately high accuracy.
• When used on testing data, you get an accurate measure of
how your model will perform and its speed.

17. 6. Parameter Tuning:
• Once you have created and evaluated your model,
see if its accuracy can be improved in any way.
This is done by tuning the parameters present in
your model. Parameters are the variables in the
model that the programmer generally decides. At
a particular value of your parameter, the accuracy
will be the maximum. Parameter tuning refers to
finding these values.

18. 7. Making Predictions
• In the end, you can use your model on unseen data to make
predictions accurately.

19. THANK YOU