The document discusses a machine learning model for detecting brain stroke from medical images. It presents the existing approaches that use algorithms like logistic regression and naive bayes that require large memory and provide inaccurate results. The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. It standardizes the brain stroke dataset and evaluates the performance of different classifiers. The random forest classifier provided the highest accuracy among the models for detecting brain stroke.

1. KUPPAM ENGINEERING COLLEGE

2. ABSTRACT:
• The brain, which is encased by the skull and consists of the cerebrum,
cerebellum, and brainstem, is a tremendously complicated and fascinating
organ in the human body.
• Stroke is the world's second greatest cause of death, so it must be treated as
soon as possible to avoid brain damage.
• ML can be used to analyze medical images, such as CT or MRI scans, to
detect signs of brain stroke. ML algorithms can be trained to identify patterns
in the images that indicate the presence of a stroke, and can do so faster and
more accurately than human experts.

3. • A brain stroke dataset was employed to build up the model. The
standardization technique is used to standardize data.
• In the training and testing procedure, KNN, Naïve Bias, Random Forest,
SVM, CatBoost classifiers are applied.
• The performance of each classifier has been estimated by adopting
performance evaluation metrics such as accuracy, sensitivity, error rate,
false-positive rate, false-negative rate, root mean square error, and log loss.
• Based on the outcome while using the RF classifier, we can determine that
our proposed model provided the maximum accuracy.

4. INTRODUCTION:
• The most severe and deadly disease in humans has long been thought to
be brain stroke. The increased occurrence of brain stroke, which is
associated with a high death rate, poses considerable risk and burden to
healthcare systems worldwide.
• The brain is the most intricate element of the human body, as we all
know. This three-pound organ is the brain’s seat of intellect, as well as a
sensation interpreter, movement creator, and behavior controller.
• It’s a part of the brain that controls cognition, memory, emotion, touch,
motor skills, vision, breathing, temperature, hunger, and other critical
human activities.
• The brain, housed in a bone shell and kept clean by protective fluid, is the
source of all the characteristics that define our humanity. Brain stroke
occurs when blood flow to a part of the brain is restricted or reduced,

5. depriving brain tissue of oxygen and nutrients.
• Brain cells begin to die in a minute under this circumstance The
number of people suffering from a stroke is increasing every day.
Strokes in the brain are more common in males than women,
especially in middle and older age.
• On the other hand, Stroke affects roughly 8% of children with sickle
cell disease. A stroke affects 15 million individuals globally every
year . Five million of them die, and another five million are
permanently crippled, putting a strain on families and communities

6. LITERATURE SURVEY
S.NO Journal Type with year Authors Title Outcomes
1. IEEE, 2020 G Vijayadeep1, Dr N Naga
Malleswara Rao2
A hybrid feature
extraction based
optimized random
forest learning model
for brain stroke
prediction
In This Paper is The
biggest concerns
created by noise or
feature selection issues
in stroke disorders is
disease prediction in
the vertebral column
dataset.
2. IEEE, 2020 Yun-Hsuan Chen 1,2 and
Mohamad Sawan 1
Trends and Challenges
of Wearable
Multimodal
Technologies
for Stroke Risk
Prediction
In this study, we
examine wearable-
based devices
designed for real-time
monitoring of stroke-
related physiological
markers.

7. S.NO Journal Type with year Authors TITILE OUTCOME
3. IEEE, 2019 IEEE, 2019
Tianyu Liu 1, Wenhui Fan
2, Cheng Wu
A hybrid machine
learning approach to
cerebral stroke prediction
based on imbalanced
medical dataset
The approach suggested
in this research
effectively reduced the
false negative rate while
maintaining a reasonably
high overall accuracy,
implying a successful
reduction in the
misdiagnosis rate for
stroke prediction.
4. IEEE, 2022 Bonna Akter
Aditya Rajbongsh
A Machine Learning
Approach to Detect the
Brain Stroke Disease
Identifying the risk of
brain stroke with
reasonable precision,
regardless of social or
cultural background,
could have a considerable
impact on human long-
term death rates. Early
detection is critical to
achieving this goal.

8. EXISTING SYSTEM:
EXISTING METHOD
• With the increasing popularity of machine learning, computer approaches
are classified into two categories: traditional methods and machine learning
methods. This section describes the related works of brain stroke detection
categorization. Machine Learning Model Detection and how machine
learning methods outperform older methods. For model development, the
present procedure in this project has a specific flow. In the existing system,
methods such as logistic regression and naive bias are applied. However, it
necessitates a huge memory and produces inaccurate results.

9. Disadvantages:
• Accuracy low
• Requires more time
• Difficult to handle

10. PROPOSED SYSTEM
• Many machine learning algorithms are available for prediction and
diagnosis of a brain stroke, including KNN, Decision Tree, Random Forest,
Multi-layer Perceptron (MLP), SVC, and CatBoost. We employed the
recommended Analysing Brain Stroke data.
• At this step, we have implemented the CatBoost Classifier algorithm on
these datasets and the individual algorithms, and then we have implemented
the Voting Ensemble method to combine these findings and compute the
final accuracy.

11. Advantages:
• Highest accuracy
• Reduces time complexity.
• Easy to use

12. PROPOSED MODULE

13. HARDWARE AND SOFTWARE REQUIREMENTS
Server side:
• Processor : i7/Intel Processor
• Hard Disk :512 GB
• RAM :16 GB
• connection- :Gigabit Ethernet
• Clock Speed : 3.6GHz
• System Type : 64-bit Operating System
• GPU : DirectX 11 graphics card with 1GB video RAM

14. Client side
• RAM : 4GB(minimum)
• Storage : 32GB
• Processor : Qualcomm Snapdragon 732G,Meadiatek etc.
• GPU : Adreno 618
• Battery Capacity : 4000 mAh Li-polymer
• Display Resolution : 2400*1080 Pixels
• Devices : Mobiles, Laptops, Desktops.

15. S/W Configuration:
• Operating System : Windows 10 .
• Server side Script : HTML, CSS & JS.
• IDE : Pycharm.
• Libraries Used : Numpy, IO, OS, Django, keras.
• Technology : Python 3.6+.

16. THANK YOU