This document describes a project to develop a machine learning model for predicting the risk of brain stroke. It used a random forest algorithm trained on a dataset of patient attributes. The model achieved promising results in accurately predicting the likelihood of stroke. The project aims to create a user-friendly application with a frontend in Python and backend in MySQL to analyze stroke data and provide risk predictions. This could help enable early detection and prevention of strokes to improve outcomes. Future work may include integrating the model with electronic health records for real-time prediction capabilities.

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Brain Stroke
Prediction
Group Name: Crocodiles
Submitted by:
Name Abdullah Al Miraj
ID: 2103910202106
Name Mohammad Sakib Khan
ID: 2103910202107
Name Tufail Ahamad Babo
ID: 2103910202123
Submitted to:
Avisheak Das
Lecturer,
Dept. of Computer Science and Engineering
Premier University, Chittagong

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Contents
 Abstract
 Introduction
 Objective and scope
 Importance of the project
 Symptomof brain stoke
 Machine Learning Model
 Algorithm
 Dataset
 Training Process
 Frontend & backend
 Stroke prediction by givingpatients Data
 Result
 Conclusion

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Abstract
 Stoke is destructive illness that typically influences individuals over the age
of 65 years age.
 Prediction of stroke is time consuming and tedious for doctors.
 Therefore, the project mainly aims at predicting the Chances of the
occurrence of stroke using emerging Machine learning techniques.
 Five different algorithms are used and a comparison is made for better
accuracy.
 Aim is to create an application with a user friendly interface that is easy to
navigate and enter inputs.

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Introduction
stroke is a life-threatening condition that happens when part of our brain doesn’t
have enough blood flow.
An ischaemic stroke is caused by a blockage cutting off the blood supply to the
brain. This is the most common type of stroke. A hemorrhage stroke is caused
by bleeding in or around the brain. A transient attack or TIA is also Known as a
mini-stroke.
Foods high in potassium, such as sweet and white potatoes, bananas, tomatoes,
melon, and soyabeans can help maintain blood pressure- the leading risk factor
of stroke.

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Objective and scope
 The prime object of this project is to construct a prediction
model for predicting stroke using machine learning algorithm.
 The dataset was obtain from the kaggle website “Healthcare
dataset stroke data”

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The project is important because it can help in early
detection and prevention of brain strokes, which can
potentially save lives and improve the quality of life
for individuals at risk.
Importance of the projEct

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Symptom of brain stoke

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Machine Learning Model
To train the machine learning model, we used a dataset
that consisted of various patient attributes such as age,
gender, blood pressure, and cholesterol levels. We
employed feature engineering techniques to select the
most relevant features and preprocess the data.
Additionally, we performed cross-validation to evaluate
the model's performance and fine-tuned the
hyperparameters to optimize its accuracy.
Techniques:

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
We used a supervised learning algorithm called
Random Forest for brain stroke prediction. Random
Forest is an ensemble learning method that combines
multiple decision trees to make predictions. It is
known for its high accuracy and ability to handle
large datasets with multiple features.
Algorithm

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Data set(before conversion)
Pandas don’t hibernate.
It is estimated that more than 50 million kangaroos live there. They are
Australia’s national symbol and appear on postage stamps, coins, and
airplanes.
There are more kangaroos than humans in Australia.
Koalas don’t have much energy and, when not feasting on leaves, they spend
their time dozing in the branches. Believe it or not, they can sleep for up to 18
hours a day!
Koalas are even more lazy than cats.
When winter approaches, they head lower down their mountain homes to
warmer temperatures, where they continue to chomp away on bamboo!

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Data set(After conversion)

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Training Process
The training process for the machine learning model involved several steps to ensure
accurate predictions and minimize errors. The following steps were taken:
 Data Collection
 Data Preprocessing
 Model Training

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Frontend & backend
We are using Python as the front and MySQL as the backend in a healthcare
data stroke, and the project can provide several benefits:
1. Python is popular programming language for data analysis and
visualization, which is useful in analyzing stroke data.
2. MySQL can handle large amounts of data and can be easily scaled to meet
the needs of the project.
3. Python has a wide range of libraries and frameworks that can be used to
build interactive and user-friendly interfaces for the project.
4. Overall, using Python for the front end and MySQL for the back end in a
healthcare data stroke project can provide a powerful and efficient solution
for managing and analyzing healthcare data.

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Stroke prediction by giving patients Data
Display wheather the patient has a stroke or not.

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Result:

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ConcluSion
Findings:
 Through the use of AI and machine learning
algorithms, we have successfully developed
a brain stroke prediction model.
 The model has been trained using a
comprehensive dataset and has shown
promising results in accurately predicting
the likelihood of a brain stroke.
 The prediction model takes into account
various risk factors and medical history to
provide accurate predictions.
Future Developments:
 Integration with electronic health
records (EHR) systems can
provide real-time data and
enhance the prediction
capabilities.
 The model can be expanded to
include additional risk factors and
medical conditions to provide a
more comprehensive prediction.

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Thank you!