Diabetes Prediction Using Machine Learning
                     Diabetes Prediction 
Using Machine Learning
Contents
► Introduction
► Proposed System
► Block Diagram
► Machine Learning Workflow
► Algorithms
► Results
► Conclusion and future scope
Introduction
► Diabetes is a common chronic disease that can be dangerous.
► Diabetes can be identified when blood glucose is higher than normal level, which is caused 
by high secretion of insulin or biological effects. 
► Diabetes can cause various damage to our body and can disfunction tissues, kidneys, eyes 
and blood vessels. 
► Diabetes can be divided into two categories, type 1 diabetes and type 2 diabetes.
► Patients with type 1 diabetes are normally younger with an age less then 30 years old. The 
clinical symptoms are increase thirst and frequent urination this type of diabetes cannot 
be cleared by medications as it requires therapy. 
► Type 2 diabetes occurs more commonly on middle-aged and old people, which can show 
hypertension, obesity and other diseases. with our living standards diabetes has increased 
commonly in people’s daily life. 
► So how to analyze diabetes is worth studying.
Proposed System
► Our proposed system aims at Predicting the number of Diabetes patients and eliminating 
the risk of False Negatives Drastically. 
► In proposed System, we use Random forest, Decision tree, Logistic Regression and 
Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or 
not.
► Random Forest and Decision Tree are the algorithms which can be used for both 
classification and regression. 
► The dataset is classified into trained and test dataset where the data can be trained 
individually, these algorithms are very easy to implement as well as very efficient in 
producing better results and can able to process large amount of data. 
► Even for large dataset these algorithms are extremely fast and can able to give accuracy 
of about over 90%. 
Introduction to Machine Learning
Block Diagram
Prediction
Testing 
Dataset
Data Algorithm 
Model
Evaluation
Training 
Dataset
Production 
data
Machine Learning Workflow
We can define the machine learning workflow in 5 stages.
► Gathering data
► Data pre-processing
► Researching the model that will be best for the type of data
► Training and testing the model
► Evaluation
► The machine learning model is nothing but a piece of code which an engineer or data scientist 
models by training it with the data according to the need of the project 
► Making the model learn through the data and allowing it to predict or give the solution that we 
want whenever we ask it to give. 
► So, whenever we give our model the new data which we want it to predict, we will get the 
predicted value according to the model training.
► The trained model might or might not perform well on the test data that we want it to predict, due 
to various reasons, 
► So before trying to train any model we need to make sure that the algorithm that is going to use is 
appropriate for the desired class that we want to predict and based on the data that we are using.
Overview of the Machine Learning Models
Training and Testing the model.
► Training is the most important part, where we train our model using the data available and 
make the machine learn and understand the data.
► When the model has learned from the data, we provide the model with another dataset to 
evaluate how good our model is performing, if it is performing well, we then test the 
model using test data, where we get to know the final performance of our model, which 
can be measure using various metrics, such as Accuracy, recall, precision, and through 
classification report.
► This whole process of building and deploying a model is done using 3 different datasets 
which are split using train_test_split(), which are ‘Training data’, ‘Validation data’, and 
‘Testing data’.
Algorithms Used
Algorithms(1/3)
The Random Forest Classifier
► Random Forest is a popular machine learning algorithm 
that belongs to the supervised learning technique. It is 
one of the widely used algorithms, which perform well 
with any kind of dataset, be it classification or 
regression. 
► It is based on the concept of ensemble learning, which 
is a process of combining multiple classifiers to solve a 
complex problem, and at the end, the results are either 
made an average of all the classifiers or mode of all the 
classifiers.
► The greater number of trees in the forest leads to higher 
accuracy and prevents the problem of overfitting.
Algorithms(2/3)
Decision Tree 
► Decision tree, as the name suggests, creates a branch of nodes
► Where each internal node denotes a test on an attribute, each 
branch represents an outcome of the test, and the last nodes 
are termed as the leaf nodes 
► Leaf node means there cannot be any nodes attached to them, 
and each leaf node (terminal node) holds a class label. 
► The decision tree is one of the most popular algorithms in 
machine learning, it can be sued for both classification and 
regression.
► There are some exceptions to decision tree also, in terms of 
data scaling and data transformation, since decision tree 
works like a flowchart in the form of branches doing data 
transformation and scaling might be optional.
Algorithms(3/3)
Logistic Regression
► Logistic regression models a relationship between predictor variables 
and a categorical response variable. 
► Logistic regression helps us estimate a probability of falling into a 
certain level of the categorical response given a set of predictors. 
► We can choose from three types of logistic regression, depending on 
the nature of the categorical response variable.
► Binary Logistic Regression:
► Used when the response is binary (i.e., it has two possible outcomes). 
► Nominal Logistic Regression:
► Used when there are three or more categories with no natural ordering 
to the levels. 
► Ordinal Logistic Regression:
► Used when there are three or more categories with a natural ordering to 
the levels, but the ranking of the levels do not necessarily mean the 
intervals between them are equal. 
Algorithm(4/4)
► Gradient Boosting Classifier
► Gradient boosting is a powerful ensemble machine learning algorithm.
► It’s popular for structured predictive modeling problems, such as classification and 
regression on tabular data, and is often the main algorithm or one of the main 
algorithms used in winning solutions to machine learning competitions, like those on 
Kaggle.
► There are many implementations of gradient boosting available, including standard 
implementations in SciPy and efficient third-party libraries. Each uses a different 
interface and even different names for the algorithm.
Results
Logistic Regression
Decision Tree
Random Forest
Gradient Boosting Classifier
Correlation Diagram
Pair Plot
Missing Values
Outcome Variable
Density Plot
Conclusion
► As per the main objective of the project is to classify and identify Diabetes Patients 
Using ML algorithms is being discussed throughout the project. 
► we build the model using some machine learning algorithms such as logistic regression, 
decision tree, Random Forest and Gradient Boosting, these all are supervised machine 
learning algorithm in machine learning.
► As part of the future scope, we hope to try out different algorithms to optimize the 
feature output process, increase the feature similarity of data to improve the model's 
representation capability.
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