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1. JAYA ENGINEERING COLLEGE
THIRUNINRAVUR
DEPARTMENT OF COMPUTER APPLICATIONS
ANNA UNIVERSITY - CHENNAI
MC5411- PROJECT WORK
PROJECT VIVA EXAMINATION

2. Early detection of chronic kidney disease
USING MACHINE LEARNING
Name: Vallarasu .TK
Regno: 110822622050
Project Guide Name: Mrs.I.Nancy
Designation: Asst.Professor

3. ABSTRACT
Chronic kidney disease prediction is one of the most important issues in
healthcare- analytics. The most interesting and challenging tasks in day-to-
day lives as one third of the adult population is affected by chronic kidney
disease (CKD), and millions die each year because they do not have access to
affordable treatment. Chronic Kidney Disease can be cured, if treated in the
early stages. The main aim of the project is to predict whether the patient
have chronic kidney disease or not in a painless, accurate and faster way
based on certain diagnostic measurement like Blood Pressure (BP), Albumin
(Al) etc., and then appropriate treatment can be given based on the details
provided by the ML model.

4. EXISTING SYSTEM
 Chronic Kidney Disease (CKD) is a serious medical condition that is
curable.
 Most individuals are unaware that the various medical tests we
undergo for various reasons may provide important information about
kidney disorders.
 As a result, characteristics of numerous medical tests are examined to
see which characteristics might contain useful information about the
disease.
 If chronic kidney disease is addressed early on, it may be cured.

5. PROPOSED SYSTEM
 Regular blood tests for Glomerular Filtration Rate (GFR) and Creatinine For the
standard techniques of early detection .
 Improvised Machine Learning Model with High Accuracy of Confusion Matrix.
 Datasets are trained by Clinical person Records to attain the best case results
on Detection.
 The User Interface can Keep track of User by pre-sized information
 Prevention of CKD are Instructed on UI as well as with support care of
individual with chat.
 Dashboard Can Motivate the User with Visualization

6. SYSTEM
REQUIREMENTS:Software
 OS: Windows 11
• Software:
Python 3.9
Flask Framework
HTML,CSS & Javascript
MS-SQL

7. DEVICE REQUIREMENTS
 Hardware Requirements:
 CORE i5
 8GB RAM

8. LIST OF MODULES :
1.Collecting Data
2.Preparing the Data
3.Training the Model
4.Evaluating the Model
5.Making Prediction

9. 1. Collecting Data:
 It is 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

10. 2. Preparing the Data:
 Putting together all the data you have and randomizing it.
 Cleaning the data to remove unwanted data, missing values, rows, and
columns, duplicate values, data type conversion, etc.
 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

11. 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.
.

12. 4. Training the Model:
 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.

13. 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.

14. 6. Parameter Tuning:
 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

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

16. THANK YOU……