ppt presentation for diabetes prediction using machine learning, This is a classification problem of supervised machine learning. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Machine learning can help people make a preliminary judgment about diabetes according to their daily physical examination data, and it can serve as a reference for doctors . So in this study, LogisticRegression and DecisionTree are implemented to predict the diabetes

1. Title
1
Presented By:
k.Surendra(190304105118)
S.PavanSundar(190304105131)
V.Niteesh(190304105139)
A.SaiKiran(190304105141)
Supervisedby:
GuideName:Assistantprof
Gaurav Varshney
DIABETES PREDICTION

2. OUTLINE
• Introduction
• Objectives of Project Work
• Literature Survey
• Problem Statement
• Implementation with Results (till date)
• Conclusion & Future Work
• References
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3. 3
INTRODUCTION
 This is a classification problem of supervised machine learning. The
objective is to predict whether or not a patient has diabetes, based
on certain diagnostic measurements included in the dataset.
 Diabetes is a common chronic disease and poses a great threat to
human health. The characteristic of diabetes is that the blood
glucose is higher than the normal level, which is caused by
defective insulin secretion
 Diabetes can lead to chronic damage and dysfunction of various
tissues, especially eyes, kidneys, heart, blood vessels and nerves
 Machine learning can help people make a preliminary judgment
about diabetes according to their daily physical examination data,
and it can serve as a reference for doctors .
 So in this study, LogisticRegression and DecisionTree are

4. 4
OBJECTIVES OF
PROJECT WORK
 The Objective of this project is to develop a system which can perform early
prediction of diabetes for a patient with a higher accuracy by using machine
learning techniques.
 To be able to perfectly implement and study the ML algorithms like Logistic
Regression, Decision Tree, Naïve Bayes, KNN.
 To be able to deal with missing values and preprocess of the dataset
 To be able to Visualize the data and make analysis out of it.
 In this project we are expecting to get an accuracy above 70%.
 To make myself upgraded with the new technologies and learn ML algorithms

5. LITERATURE
REVIEW
5
Sl.
No
Paper Title Journal
with Year
Methods Shortcomings
1 SVM 2014 find the best straight line
bw two classes
Stright line bw
classes
2 Classification and
Diagnosis of Diabetes
2015 classifiers that have been
used only once to predict
diabetes
Predict diabetics
3 Implementation Dataset 2019 Achiev trained model with
highest accuracy
High accuracy
4 Data mining and
classification
2019 measure of the dataset Data set
measure
5 Decision Tree 2018 help you to evaluate
your options.
Our opinions
6 screening process of BP
neural network
2017 We use test samples to
evaluate the generalize
ability of the model
Evaluate model

6. PROBLEM
STATEMENT
Diabetes mellitus is a common disease that affects a vast
majority of the people in many parts of the world. Diabetes
affects people usually after the age of 20. According to WHO
statistics, the global prevalence of diabetes among adults
above 18 years of age has risen to 8.5% in 2014. Diabetes
prevalence has been increasing more in middle and
lowincome countries. It becomes a cause for other illnesses
also like blindness, kidney failure, cholesterol and heart
diseases. The deaths due to diabetes and high blood glucose
are on the rise. Prediction of diabetes at an early stage would
help the patients to maintain the sugar level under control.
As data mining techniques prove to be good in predictive
analyses, a data mining approach is used to predict the risk
of diabetes in the proposed approach. The performance of
the algorithm is also measured and improved using feature
selection and selection of training set
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7. IMPLEMENTATION WITH
RESULTS (TILL DATE)
1)Logistic Regression the Accuracy score is : 76.6%
2) KNN the Accuracy score is :69%
3) Naive Bayes
1)Gaussian the accuracy score is :71%
2) Multinational the accuracy score is :57%
3) Bernoulli the accuracy score is: 61%
4) Random forest the accuracy score is : 75%
5) (i) Decision Tree using Gini Index Method:
The accuracy score is : 73.3%
(ii)Decision Tree using Entropy Method:
The accuracy score is : 76%
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8. TOOLS AND TECHNOLOGY
8
We have used
python==> version(3.9.2)
numpy ==> version (1.19.0)
pandas==> version (1.4.3)
matplotlib==> version (3 .1)
seaborn ==> version (11.0)
Sklearn ==> version (0.23)

9. 9
COMPARISON OF
EXISTING ALGORITHMS

10. 10
CONCLUSION &
FUTURE WORK
 The main aim of this project was to design and implement
Diabetes Prediction Using Machine Learning Methods and
Performance Analysis of that methods and it has been achieved
successfully.
 Successfully able to clean the data and split it into training and
testing data
 The proposed approach uses various classification and
ensemble learning method in which Decision Tree, Logistic
Regression are used.
 75% classification accuracy has been achieved.
 The Experimental results can be asst health care to take early
prediction and make early decision to cure diabetes and save
humans life.
 In future I would like to move on to Deep Learning and
upgrading myself with new technologies like TensorFlow and
keras and NeuralNetwork and continue my research in the field
of AI

11. REFERENCES
11
[1] Gupta, Manoj Kr, and Pravin Chandra. "A comparative study of clustering algorithms." 2019 6th International
Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2019.
[2] Dudukovich, Rachel, and Christos Papachristou. "Delay tolerant network routing as a machine learning classification
problem." 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS). IEEE, 2018.

12. Thank
you
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