Brain stroke prediction using machine learning. The GUI is made using HTML, CSS, Flask.

Brain stroke prediction using machine learning It consists of several components, including data preprocessing, feature extraction, machine learning model training, and prediction. It's much more Moreover, machine learning models can integrate multiple clinical parameters and risk factors to generate personalized risk scores, aiding in individualized patient management. Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. One key improvement is the refinement of deep learning models to The brain, which comprises the cerebrum, cere-bellum, and brainstem and is covered by the skull, is a very complex and intriguing organ in the human body. Brain stroke recognition using MRI reports was the subject of research by Kim et al. A stroke, also known as a cerebrovascular accident or CVA is when part of the brain loses its blood supply and the part of the body that the blood-deprived brain cells control stops working. From Figure 2, it is clear that this dataset is an imbalanced dataset. Stroke is a leading cause of disability and death worldwide, often resulting from the sudden disruption of blood supply to the brain. B. 003 In , a natural language processing (NLP)-based machine learning (ML) algorithm can predict adverse outcomes in acute ischemic stroke patients (AIS) using brain MRI maps. Our work also determines the importance of the characteristics available and determined by the dataset. Eur J Neurol. . Frequency of machine learning classification algorithms used in the literature for stroke prediction. , Raman B.  · This project studies the use of machine learning techniques to predict the long-term outcomes of stroke victims. Problems with data pre-processing and balancing, global data, structured prediction, and insufficient data for training remained unsolved. Caution Alert! Since the data of BMI levels Above is too extrapolated, it's not safe to fill using just one category with the remaining missing values This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. Brain stroke, also known as a cerebrovascular accident, is a critical medical condition that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from The organ known as the brain, which is securely protected within the skull and consists of three main parts, namely the cerebrum, cerebellum, and brainstem, is an incredibly complex and intriguing component of the human body. 4 , 635–640 (2014). A [4], Prasanth. The proposed work aims at designing a model for The dataset used in this project contains information about various health parameters of individuals, including: id: unique identifier; gender: "Male", "Female" or "Other"; age: age of the patient; hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension; heart_disease: 0 if the patient Stroke Risk Prediction Using Machine Learning: • Input features: Age, hypertension, glucose levels, smoking status, BMI, and lifestyle factors. The use of Artificial Intelligence (AI) methods (Big Data Analytics, ML, and Deep Learning) as predictive tools is particularly important for brain diseases (e. This document describes Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. The primary objective of this study is to develop and validate a robust ML model for the prediction and early detection of stroke in the brain. Prediction of stroke thrombolysis outcome using CT brain machine learning. Various data mining techniques are used in the healthcare industry to This document presents a project that aims to predict the chances of stroke occurrence using machine learning techniques. Stroke risk is the likelihood or probability that an individual Keywords—Accuracy, Data preprocessing, Machine Learning, Prediction,Stroke I. Stroke Prediction Using Machine This study focuses on the intricate connection between general health, blood pressure, and the occurrence of brain strokes through machine learning algorithms. 02. We identify the most important factors for stroke prediction. et al. Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. According to the WHO, stroke is the 2nd leading cause of death worldwide. Stroke, a condition that ranks as the second leading cause of death worldwide, Stroke, a leading cause of disability and mortality globally, is a medical condition characterized by a sudden disruption of blood supply to the brain which can have severe and often lasting effects on various functions controlled by the affected part of the brain, such as movement, speech, memory and other cognitive functions 1,2. A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches Wu Y, Fang Y. The current work predicted the stroke using the different machine learning models namely, Gaussian Naive Bayes, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbours, AdaBoost Classifier, XGBoost Classifier, and Random This paper is based on the prediction of brain stroke using machine learning algorithms which helps to rehabilitate the patient so that one can gain their life back to normal. The data used in this study included 5110 patients with 12 attributes. 2. LITERATURE REVIEW Many researchers have already used machine learning based approached to predict strokes. ; Didn’t eliminate the records due to dataset being highly skewed on the target attribute – stroke and a good portion of the missing BMI values had accounted for [4] “Prediction of stroke thrombolysis outcome using CT brain machine learning” - Paul Bentley, JebanGanesalingam, AnomaLalani, CarltonJones, KateMahady, SarahEpton, PaulRinne, PankajSharma, OmidHalse, AmrishMehta, DanielRueckert - Clinical records and CT brains of 116 acute ischemic stroke patients In most of the previous works machine learning-based methods are developed for stroke prediction. Am. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. The accuracy of the naive Bayes Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. The SMOTE technique has been used to balance this dataset. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. & Al-Mousa, A. In today's era, the convergence of modern technology and healthcare has paved the path for novel diseases prediction and prevention technologies. K. Sun C, Zhao Z, Wang F, Zheng X, et al. 2014. 1016/j. The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the data used in this study 38,39. Five machine learning techniques were applied to the Cardiovascular Health Study (CHS) dataset to forecast strokes. Brain strokes, a major public health concern around the world, necessitate accurate and prompt prediction in order to reduce their devastation. It is a big worldwide threat with serious health and economic implications. Machine learning (ML) techniques have been extensively used in the healthcare industry to build predictive models The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. In the work presented by Tahia Tazin et al. On the other Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. S [5] Department of Artificial Intelligence and Data Science, Sri Sairam Engineering College - Chennai ABSTRACT Brain stroke is one of the driving causes of death and disability worldwide. The brain stroke prediction module using machine learning aims to predict the likelihood of a stroke based on input data. A stroke is a Brain Stroke is considered as the second most common cause of death. Before building a model, data preprocessing is required to remove unwanted noise and outliers from the dataset that could lead the model to depart from its intended training. There was an imbalance . Something went wrong and this page crashed! This research improved the prediction accuracy of the Stroke Predictor (SPR) model using machine learning techniques improved the prediction accuracy to 96. G [2], Aravinth. Reason for topic Strokes are a life threatening condition caused by blood clots in the brain, and the likelihood of these blood clots can increase based on an individual's overall Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. In any of these cases, the brain becomes damaged or dies. When part of the brain does not receive sufficient blood flow for functioning a brain stroke strikes a person. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. Preprocessing. A Stroke occurs when a blood vessel is either blocked by a clot or bursts. Stroke, a leading neurological disorder worldwide, is responsible for over 12. Mutual Fig. 1111/ene. Natural language processing (NLP), Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. Prediction of brain stroke using clinical attributes is prone to errors and takes The brain is the human body's primary upper organ. 2 million new cases each year. This research focuses on predicting brain stroke using machine learning (ML) and Explainable Artificial Early prediction of brain stroke has been done using eight individual classifiers along with 56 other models which are designed by merging the pairs of individual models using soft and hard voting Machine Learning techniques including Random Forest, KNN , XGBoost , Catboost and Naive Bayes have been used for prediction. Bentley P, Ganesalingam J, Carlton Jones AL, Mahady K, Epton S, Rinne P, et al. doi: 10. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. It occurs when there is a sudden interruption or reduction of blood supply to the brain, leading to the impairment of brain function. Brain Stroke Prediction Using Machine Learning - written by Latharani T R, Roja D C, Tejashwini B R published on 2023/07/07 download full article with reference data and citations Brain Stroke Prediction Using Machine Learning. predicting the occurrence of a stroke can be made using Machine Learning. 1. M. This research investigates the Declaration We hereby declare that the project work entitled “Brain Stroke Prediction by Using Machine Learning” submitted to the JNTU Kakinada is a record of an original work done Brain Stroke Prediction Using Machine Learning 299 classifiers. The rest of the paper is organized as follows: In section II, we present a summary of related work. deep-learning pytorch classification image-classification ct-scans image-transformer vision Brain Stroke Detection And Prediction Using Machine Learning 1 Prof. To understand the process of e-learning let's imagine that It is one of the major causes of mortality worldwide. Stroke prediction with machine learning methods among older Chinese. Stroke is considered as medical urgent situation and Al-Zubaidi, H. [Google Scholar] 17. The dataset of 11 clinical features is used as input in Volume 13, Issue 07, Jul 2023 ISSN 2457-0362 Page 800 BRAIN STROKE PREDICTION USING MACHINE LEARNING ALGORITHMS Ms. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. Introduction: “The Machine learning (ML) techniques have gained prominence in recent years for their potential to improve healthcare outcomes, including the prediction and prevention of stroke. NeuroImage Clin. A stroke is a medical emergency when blood circulation in the brain is disrupted or outflowing due to a burst of nerve tissue. [] an algorithm based on Random Forest, Decision tree, voting classifier, and Logistic regression machine learning algorithms is built. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. nicl. , who investigated machine learning techniques. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. 97% when compared with the existing models. Data-level algorithms outperform single-word or deep-sentence (DL) algorithms (such as multi-CNN and CNN algorithms) in predicting clinical This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. This document summarizes a student project on stroke prediction using machine learning algorithms. Roja D C Dept of CSE Jit, Bentley P, Ganesalingam J, Carlton Jones AL, Mahady K, Epton S, Rinne P, et al. P [3], Elamugilan. Our contribution can help predict early signs and prevention of this The Algorithm leverages both the patient brain stroke dataset D and the selected stroke prediction classifiers B as inputs, allowing for the generation of stroke classification results R'. 3. ANITHA1, Ms. They preprocessed the data, addressed imbalance, and performed feature engineering. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. (2014) 4:635–40. To address this challenge, we propose a novel meta-learning framework that integrates advanced hybrid The concern of brain stroke increases rapidly in young age groups daily. Published in: 2023 Fifth International Conference on Electrical, Abstract: Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. The algorithms present in Machine Learning are constructive in making an accurate prediction and give correct analysis. Stroke prediction using machine learning classification methods. The heterogeneity between studies, the high risk of bias and the lack of external validation emphasize that although much progress is witnessed using machine learning algorithms in predicting stroke their implementation in the real-world setting is limited and the use of ML for stroke mortality prediction is still in the Stroke, a cerebrovascular event, represents a significant global health concern due to its substantial impact on morbidity and mortality. Neurol. 5 million Chinese adults. Diagnosis at the proper time is crucial to saving lives through immediate treatment. Govindarajan et al. Data-level algorithms outperform single-word or deep-sentence (DL) We propose a predictive analytics approach for stroke prediction. for accurate and efficient brain stroke prediction using deep learning techniques. Ingale, 3Amarindersingh G. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific Bentley, P. This study Feature extraction is a key step in stroke machine-learning applications, as machine-learning algorithms are widely used for feature classification and prediction. Leveraging the power of machine learning, this paper presents a systematic approach to Soft voting based on weighted average ensemble machine-learning methods for brain stroke prediction utilizing clinical variables gathered from the University of California Irvine Machine Learning Repository(UCI) repository, which has 4981 rows and 11 columns, was proposed in a research study [17]. [8] The title is "Automated Classification of Stroke The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. Implementing a Brain strokes are a leading reason of affliction & fatality globally, and timely diagnosis is critical for successful treatment. Overall, this observe demonstrates the effectiveness of A-Tuning Ensemble machine learning in stroke prediction and achieves excellent outcomes. 2020;27:1656–1663. KADAM1, PRIYANKA AGARWAL2, been created which would alert the person using about a probable future brain stroke and further suggests to consult a medical professional. Age, heart disease, average glucose level research that predict stroke risk variables and outcomes using a variety of machine learning algorithms, like random forests, decision trees also neural networks. ˛e proposed model achieves an accuracy of 95. The leading causes of death from stroke globally will rise to 6. P [1], Vasanth. OK, Got it. Using the publicly accessible stroke prediction dataset, the study measured four commonly used machine learning methods for predicting brain stroke recurrence, which are as follows: (i) Random forest (ii) Decision tree (iii) This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average This research paper focuses on predicting brain stroke occurrence using a range of machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. Brain stroke detection and prediction systems can be enhanced through advancements in AI and medical technology. This causes the brain to receive less oxygen and nutrients, which damages brain cells begin to deteriorate. When brain The brain is the most complex organ in the human body. Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. ARUNA VARANASI3, ADIMALLA PAVAN KUMAR4, BILLA CHANDRA KIRAN5, V. 2022 international Arab conference on information technology (ACIT) 1–8 (IEEE, 2022). By applying machine learning algorithms to stroke, we developed a novel approach to diagnosis and treatment that surpasses manual judgment in sensitivity and significantly improves When a blood vessel supplying to the brain is obstructed or blocked because of a blood clot called an ischemic stroke which is accounting for 87% of all strokes according to the American Heart Stroke risk prediction using machine learning: A prospective cohort study of 0. Eur. Firstly, the authors in applied four machine learning algorithms, such as naive Bayes, J48, K-nearest neighbor and random forest, in order to detect accurately a stroke. The machine learning algorithms for stroke Machine Learning for Brain Stroke: A Review Manisha Sanjay Sirsat,* Eduardo Ferme,*,† and Joana C^amara, *,†,‡ Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. Jare 1 Computer Engineering Department, 1 Nutan Maharashtra Institute of Engineering and Technology, Talegaon Pune, India The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. Nowadays, it is a very common disease and the number of patients who attack by brain stroke is skyrocketed. An ML model for predicting stroke using the machine learning technique is presented in 1 INTRODUCTION. Neuroimage Clin. For accurate prediction, the study used ML calculations such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Navies Bayes (NB), and Support Vector Machine (SVM), and deploy Total number of stroke and normal data. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. Among In this paper, we focused on finding importance of features and considering the features that are best for brain stroke prediction. This study provides a Explore and run machine learning code with Kaggle Notebooks | Using data from National Health and Nutrition Examination Survey. Int J Early Prediction of Brain Stroke Using Machine Learning Kalaiselvi. The review sheds light on the state of research on machine learning-based stroke prediction at the moment. 1-3 Deprivation of cells A stroke is caused by damage to blood vessels in the brain. It is the world’s second prevalent disease and can be fatal if it is not treated on time. In the data preprocessing module, the So, it is imperative to create a novel ML model that can optimize the performance of brain stroke prediction. Hence, we predict stroke using machine learning and XAI methods. According to “United States centers for Disease Control and Prevention” around 7,95,000 people have been affected with strokes in the year 2018 [1]. Brain Stroke Prediction Using Machine Learning and Data Science VEMULA GEETA1, T. An early intervention and prediction could Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. Early detection of a brain stroke can efficient in the decision-making processes of the prediction system, which has been successfully applied in both stroke prediction [1-2] and imbalanced medical datasets [3]. An application of ML and Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. It primarily occurs when the brain's blood supply is disrupted by blood clots, blocking blood flow, or when blood vessels rupture, causing bleeding and damage to brain tissue. We use machine learning and neural networks in the proposed approach. These models are trained and evaluated using appropriate performance metrics to identify the most accurate algorithm Machine learning for brain-stroke prediction: comparative analysis and evaluation Article 20 August 2024. The dataset is in comma separated values (CSV) format, including Using a machine learning algorithm to predict whether an individual is at high risk for a stroke, based on factors such as age, BMI, and occupation. This loss of blood supply can be ischemic because of lack of blood flow, or haemorrhagic because of bleeding into brain tissue. The accurate prediction of brain stroke is critical for effective diagnosis and management, yet the imbalanced nature of medical datasets often hampers the performance of conventional machine learning models. Gautam A. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification In this section, we will present the latest works that utilize machine learning techniques for stroke risk prediction. When brain Machine Learning Models: The repository offers a range of machine learning models, including decision trees, random forests, logistic regression, support vector machines, and neural networks. This results in approximately 5 million deaths and Stroke is one of the most serious diseases worldwide, directly or indirectly responsible for a significant number of deaths. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. J. S. g. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. INTRODUCTION When a blood vessel bleed or blockage lowers or stops the flow of blood to the brain, a stroke ensues. As the second leading cause of death globally, Keywords: brain stroke, deep learning, machine learning, classification, segmentation, object detection. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. Med. MAMATHA2, DR. Prediction of brain stroke using clinical attributes is prone to errors and takes lot of time. A predictive analytics approach for stroke prediction using machine learning and neural networks Soumyabrata Deva,b,, Hewei Wangc,d, Chidozie Shamrock Nwosue, Nishtha Jaina, Bharadwaj Veeravallif, Deepu Johng aADAPT SFI Research Centre, Dublin, Ireland bSchool of Computer Science, University College Dublin, The purpose of this work is to demonstrate whether machine learning may be utilized to foresee the beginning of brain strokes. Smita Tube, 2 Chetan B. Informatics Authors visualization 7. Biomed. Very less works have been The most common disease identified in the medical field is stroke, which is on the rise year after year. The GUI is made using HTML, CSS, Flask. Ischemic strokes are the most common, this is the first study that used all of these different attributes to build a prediction model using machine Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. This study proposes an accurate predictive model for identifying stroke risk factors. Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. In recent years strokes are one of the leading causes of death by affecting the central nervous system. Gulati, 4Pranav M. Latharani T R Dept of CSE Jit, Davangere. To get the best results, the authors combined the Decision Tree with the C4. , stroke occurrence), since, in many cases, until all clinical symptoms are manifested and experts can make a definitive diagnosis, the results are essentially irreversible. PAVANI 2, Mr. 49% and can be used for early A brain stroke happens when blood flow to a part of the brain is interrupted or reduced. We get a total accuracy of 97%. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. Section III explains our proposed intelligent stroke prediction framework. Article PubMed PubMed Central Google Scholar Machine learning (ML) as a subfield of Artificial Intelligence (AI) [] is widely used in last years in different fields, mainly in complex situations needing automatic process [], such as the domain of medicine and healthcare []. A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. 5 approach, Principal Component Analysis, In , a natural language processing (NLP)-based machine learning (ML) algorithm can predict adverse outcomes in acute ischemic stroke patients (AIS) using brain MRI maps. Ten Prediction of stroke is a time consuming and tedious for doctors. In [6], this paper presents a stroke diagnosis model using hybrid machine learning Hemorrhagic stroke occurs because of a burst vessel that leads to bleeding in the brain, whereas ischemic stroke occurs because of a blockage of the arteries of the brain. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention In a human life there are alot of life-threatening consequences, one among those dangerous situations is having a brain stroke. In this work, we present a comprehensive study on stroke prediction using a range of machine learning techniques. Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. It is a big worldwide threat with serious health and economic Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. 14295. It is one of the major causes of mortality worldwide. Five different algorithms are used and compared to achieve better accuracy. Machine learning techniques show good accuracy in predicting the likelihood of a stroke from related factors. The works previously performed on stroke mostly include the ones on Heart stroke prediction. There are two primary causes of brain stroke: a blocked conduit (ischemic  · Stroke is a disease that affects the arteries leading to and within the brain. Machine learning (ML) based prediction models can reduce the fatality rate by detecting Only BMI-Attribute had NULL values ; Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median. From 2007 to 2019, there were roughly 18 studies associated with stroke diagnosis in the subject of stroke prediction using machine learning in the Prediction of Brain Stroke Severity Using Machine Learning machine learning, stroke prediction, subarachnoid hemorrhagic stroke. lathaquick@gmail. When the supply of blood and other nutrients to the brain is interrupted, symptoms II. This is most often due to a blockage in an artery or bleeding in the brain. This report explores the use of Machine Learning (ML) techniques to predict the likelihood of stroke based on patient health data. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. [11] con-ducted a study to categorize stroke disorder using a Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. The objective is to create a user-friendly application to predict stroke risk by entering patient data. , Dweik, M. When the clot or bursts occur, part of the brain cannot get the blood needed, so PDF | On May 19, 2024, Viswapriya Subramaniyam Elangovan and others published Analysing an imbalanced stroke prediction dataset using machine learning techniques | Find, read and cite all the Created a Web Application using Streamlit and Machine learning models on Stroke prediciton Whether the paitent gets a stroke or not on the basis of the feature columns given in the dataset This Streamlit web app built on the Stroke Prediction dataset from Kaggle aims to provide a user-friendly In a human life there are alot of life-threatening consequences, one among those dangerous situations is having a brain stroke. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. Aim is to create an application with a user-friendly interface which is easy to navigate and enter inputs. Stroke is the world's second-leading cause of mortality; as a result, it requires prompt treatment to avoid brain damage. STROKE PREDICTION USING MACHINE LEARNING 1T M Geethanjali, 2Divyashree M D, 3Monisha S K, India Abstract: Blood vessel carries oxygen and nutrients to the brain. There have lots of reasons for brain stroke, for instance, unusual blood circulation across the brain. The students collected two datasets on stroke from Kaggle, one benchmark and one non-benchmark. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. HRITHIK REDDY6 1, 2 Assistant Professor, Department of Computer Science and Engineering, Sreenidhi Institute of Science Download Citation | Brain Stroke Prediction using Machine Learning | A brain stroke, is a saviour disease in which a blood cot or bleeding occur in the brain during a stoke, which can result in stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. AMOL K. It is a critical medical condition that demands timely detection to prevent severe outcomes, including permanent paralysis and death. The Machine Learning method observes developing a prediction model it will be used to get the solution to a given Problem Statement. Due to its smart technological advancements in data processing and analysis, a set of ML Brain Stroke Prediction Using Machine Learning Approach DR. RAJESH 3 #1 Assistant professor in the Department of Masters of Computer Applications in the SRK Institute of Technology, Enikepadu, The situation when the blood circulation of some areas of brain cut of is known as brain stroke. Introduction. com. Learn more.