PROACTIVE ANALYSIS OF BRAIN STROKE USING MACHINE LEARNING TECHNIQUES

Abstract

ABSTRACT: A brain stroke is among the medical emergencies critical in causing death. mainly nffccting older age groups and generally those with unhealthy habits. Forecasting n stroke even before it happens can, therefore, save a lot of lives. Unhealthy habits in life like smoking and too much alcohol consumption ~nd s~vere medical conditions like diabetes and high blood pressure are considered to be major risk factors for a brain stroke. This project concentrates on the design and building of a predictive model for the possibility of brain strokes before they occur. This model is trained using attributes relating to the medical data they arc captured from, such as diabetes, blood pressure, etc. A model is built using SVC algorithm. Model will be employed with an integration of a website, which would allow users to enter het~lth at I nbs to compute the risk probabilities of the stroke in real-time.

Specification

PROACTIVE ANALYSIS OF BRAIN STROKE USING MACHINE LEARNING
TECHNIQUES
FIELD OF INVENTION:
This invention would come in the domain of healthcare diagnostics, where predictive analytics
is applied in relation to the risk of a brain stroke, based on its medical parameters. This could
be an innovative approach to prevent strokes by making usc of the advancement of machine
learning and artificial intelligence. It analyses the properties involving diabetes, blood pressure,
among other things, hence making it a very good diagnostic tool to detect strokes precariously
and afford quick intervention. What's more, it merges with a user-friendly web interface that
allows users to input their medical data to receive real-time predictions about the possibility of
their stroke risk. It- is an innovative effort aimed at contributing to public health in enabling
early detection and prevention strategies for brain strokes, thus reducing the complications
The invention also underscores how machine learning can change health care through an
accurate, data-driven insight into conditions and medical decisions that may improve patient
outcomes. All types of users will be able to utilize this prcdi~tive model through medical
centers or labs. Therefore, this is a great innovation in preventive medicine in developing one
of the most effective tools that can be used against one of the leading causes of death
worldwide.
BACKGROUND OF INVENTION:
I:! rain Stroke is one of the leading causes of death in the world and it's a uredical emergency. It
is duet::> either blockage or the rupture of blood vessels that bring oxygen into the brain, which
can lead to 'evcrc outcomes, such as paralysis, numbness, partial blindness, and indeed death.
The risk factors are majorly caused due to unhealthy lifestyle habits including smoking,
excessive alcohol consumption, and other diseases like diabetes and hypertension. Therefore,
with the introduction of machine learning into health care, this nature of prediction and
prevention of diseases could be changed. Machine learning algorithms are sophisticated
programs in analysis of data, used to predict medical conditions through analytical intelligence,
thus providing early possibilities of intervention .
(00 I)
AUTHOR NAME: STERN NAFTAL et al
PATENT NO: W02022157775Al
DESCRIPTION:
A system and method of predicting occurrence of a cerebrovascular event in a human subject
may include receiving a corpus of content data elements, each representing usage of an
Internet-based data source by at least one human subject of a cohort of human subjects, at a
specific point ·in time. Embodiments may be configured to analyze the corpus ofCDEs to
extract, with respect to the at least one human subject, a feature vector representing cognitive
ability. Embodiments may apply a machine-learning based classification model on one or more
feature vectors, corresponding to CDEs of the at least one human subject, to produce
a prediction data element that may represent_ probability of occurrence, and/or evolution of a
cerebrovascular event in the at least one subject
AUTHOR NAME: MA XUESHENG AND LIU WEIQI
PATENT NO: CN 113988209A
DESCRIPTION:
The invention discloses a training method and device for a stroke prognosis prediction model.
The method comprises the following steps that: training initial data is obtained, wherein the
initiel deta comprises the clinical information of patients and the medi~al image features of the
patients; the training initial data is sampled by adopting a greedy algorithm, and the sampled
data is filtered based on a mean square error minimization method to obtain a training data set;
model training is carried out based on the training data set, an obtained stroke prognosis
prediction model can predict the prognosis mRS score condition of a patient, and the stroke
prognosis prediction model is established based on an extreme gradient lifting machine
learning method. According to the method, the clinical data information of the patients and the
imaging infonnation of the patients are combined, the machine learning model is established,
the prognosis condition of a patient suffering the stroke can be accurately predicted, so that a
better treatment prognosis prediction result of ihe patient suffering from the acute ischemic
stroke is obtained be tore treatment; and the method can be used as a powerful auxiliary tool for
adjuvant treatment.
[003)
AUTHOR NAME: LIN SHIH-YI et al
PATENT NO: TW202429481A
DESCRIPTION:
A system for applying machine !earning to carotid sonographic features for recurrent stroke
prediction model module. The preprocessing module receives and preprocesses stmctured data.
The recurrent stroke prediction model module then applies machine learning to the
preprocessed data using k-fold cross-validation method, where k is an integer greater than I.
The recurrent stroke prediction model module first divides the pre-processed structured data
into k data sets. In each ofk iterations, one of the k data sets is used as a verification set, and
the remaining k-1 data sets are used as training sets. The training sets are input into the
recurrent stroke prediction model for training, and a model evaluation is performed on the
verification set, thereby obtaining k AUC values and k models based on which a model with
the best performance is selected to be the final model
OBJECTIVES:
I.
II.
Ill.
IV.
Develop a Predictive Model: Implement a strong machine learning model that predicts
a brain stroke even before it happens based on several meciical at.trihutes such as
diabetes values, blood pressure readings, and lifestyle issues.
Integrate with User Interface: Integrate the predictive model with an interactive web
interface so users can input their health data and receive real-time risk assessments or
having a brain stroke.
Enhance Early Detection: Enabling timely medical interventions with preventive
measures would directly reduce the incidence and severity of stroke complications
through an enhanced capability for early detection.
Improve Hcalthcare Outcomes: A more accurate, data-driven v1s1on can 1mprove
informality in medical decisions using machine learning to elicit desirable patient
outcomes concerning the prevention and management of stroke.
v. Enhanced Accessibility: Build the predictive tool as accessible as possible to reach the
large population of users so that it can be used universally in clinical and personal
health practices.
VI. Promote Preventive Healthcarc: Contribute to public health by developing a
preventive tool which will support preventive strategies in healthcare, prevent lives and
provide an enhanced quality of life for those being at risk of brain stroke.
SUMMARY:
The project will develop a machine learning model for predicting pre-event possibilities of
brain strokes. It will be trained on medical attributes such as diabetes and blood pressure values
using SVC algorithm. and be embedded into an interactive web interface where users could
input their health data through laboratories and receive a real-time risk assessment. This would
be an active tool that would look roward developing early detection of stroke and reuuciug
incidence and severity of complications related to stroke, thus improving healthcare outcomes
by the usage of advanced machine learning techniques in preventive medicine.
BRIEF DESCRIPTION OF THE DRAWING:
Figure I: Data flow diagram of the Machine Learning model
i'igurc 2: The output of our model which predicts the probability of the occurrence of stroke
Figure 3: A san.ple website where users can give their inputs to check for risk of stroke
DETAILED DESCRIPTION OF THE INVENTION:
The invention uses advanced machine learning techniques for the prediction of risk due to
brain stroke. The objective behind this invention is to design a predictive model capable of
analyzing various medical attributes to predict potential brain strokes and acts as an early
warning system for that condition, thus allowing for timely intervention and preventive
measures. Brain stroke .is a serious medical emergency and one of the major causes of death in
the world. It takes place when the supplying blood to certain parts of the brain is stopped or, at
least greatly reduced, failing the necessary oxygen and nutrients needed in the different tissues
in the brain. Brain strokes can lead to severe complications such as paralysis, numbness, and
even death due to vision loss. The main potential risk factors include unhealthy lifestyle habits,
such as smoking and excessive alcohol consumption, as well as diseases like diabetes and
hypertension. The key to such complications is early prediction and intervention, which can
significantly improve the chances of recovery and reduce their severity.
The algorithm used in the invention is the Support Vector Classifier, which is a very accurate
algorithm for the classification procedure. The model was trained with an exhaustive dataset
that included the diabetes values, blood pressure readings, cholesterol levels, whether the
patient smoked, and other indicators related to health. It learns to detect the patterns of
relationship that can predict the probability of strokes developing in the brain with all these
attributes
The trained predictive model is then incorporated into an intuitive web interface that lets users
input their health data, such as current diabetes levels or blood pressure readings. The model
would then process the input data real-time and give immediate feedback on how much of a
risk someone was to having a brain stroke. Thus, the interaction approach ensures that users
receive timely feedback about their health status and therefore will be encouraged to take
proactive measures to prevent them from ever suffering strukt::s.
The system collects detailed information regarding patients, including those on brain vessel
shape and blood flow data in case it is present. This rich source of data collection enables a
more accurate and profound risk analysis. The trained risk analysis model processes the
acquired data. and information will be utilized in predicting the risk of stroke. The model gets a
risk value on how likely the person is going to have a stroke. Early intervention and preventive
measures can be taken into place for high-risk individuals.
This invention embodies the loreti·om in preventive medicine as it avails a proactive tool for
stroke prcvcmion and management. Therefore, through the adoption of these strategies by
machine learning, the system pro1·ides data-driven accurate insights that can potentially aid in
medical decision-making hence improving patient care. The ingestion of the predictive model
through an interactive and user-friendly web interface enables tnruttlvc and accessible use for a
broad-ranging universe of users. This would facilitate the possibility of forecasting potential
brain diseases, thus offering preventive healthcare measures and thereby enhancing the quality
of life among individuals at risk of brain strokes.
This is not an application in stroke prediction but rather depicts the way of how machine
learning can revolutionize health care through the availability of precise and reliable tools to be
used in the prediction and management of diseases. It brings together great algorithms with
user-friendly interfaces that bridge two very comprehensive analyses of complex data with
practical health-care application. This device embodies the principles of precision medicine, in
which treatments or preventive measures are tailored to the unique health profile of an
individual to bring about effective health results. furthermore, the predictability of the system
may be fm1hcr improved through the data and updates on techniques in machine learning. It
may retrain with more data collected from patients and make further adjustments to increase
accuracy and reliability. This way, the system can remain effective for the prediction of
potential brain strokes and other conditions due to the evolution in healthcare technology.
In conclusion, this invention provides an all-inclusive innovative approach to the risks of brain
stroke, applying machine learning for predictions. It develops a robust predictive model that
integrates an accessible web interface and puts much emphasis on the accuracy of daia through
preprocessing. The system may result in a very important tool for early intervention and
prevention in healthcare, possibly saving lives, reducing the seriousness of complications fi·mn
stroke, and ultimately having implications in public health and wellbeing.

CLAIMS:
WE CLAIM,
Claim I: We claim a machine learning model configured to analyze the preprocessed
numerical data and generate real-time predictions of stroke risk based on algorithms including
support vector machine integrated with website and provided to the medical laboratory with
the license key
Claim 2: We claim the application of machine leaming techniques in stroke risk prediction
represents a significant innovation in pr\?v.:ntivc hcalthcarc, leading to more proactive
management of patient outcomes.
Claim 3: A data collection module for acquiring numerical clinical data, including patient
demographics, medical history, and vital signs.
Clam 4: We claim a user interface that displays stroke risk predictions to healtheare providers,
allowing for immediate clinical decisions and interventions based on the analysis.
Claim 5: We claim a feedback mechanism that continuously updates the machine leaming
model. with new patient data and perfom1ance metrics to improve predic.tivc accuracy over
time.
Claim 6: We claim an exploratory data analysis module that identifies significant pattems and
correlations in the numerical data prior to model training, informing feature selection.
Claim 7: We claim an ethical ti·amework ensuring compliance with data privacy regulations
and the mitigation of bias in the model's predictions, safeguarding patient information.
Claim 8: We claim a preprocessing module that cleans, normalizes, and engineers features
from the collected numerical data to enhance the performance of predictive models
Claim 9: We claim a reporting module that generates insights and stattsttcs on stroke risk
factors and trends, supporting healthcare providers in population health management.
Claim I 0: We claim personalized recommendations derived from the predictive analysis,
including lifestyle modifications and preventive measures tailored to individual patient risk
profiles.

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