Brain stroke prediction dataset This project aims to predict the likelihood of a person having a brain stroke using machine learning techniques. The dataset used in the development of the method was the open-access Stroke Prediction dataset. Dec 1, 2021 · The objective is to create a user-friendly application to predict stroke risk by entering patient data. Learn more. 968, average Dice coefficient (DC) of A stroke occurs when the blood supply to a person's brain is interrupted or reduced. We use prin- Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. In the brain stroke dataset, the BMI column contains some missing values which could have been filled Apr 21, 2023 · Brain stroke prediction using machine learning machine-learning logistic-regression beginner-friendly decision-tree-classifier kaggle-dataset random-forest-classifier knn-classifier commented introduction-to-machine-learning xgboost-classifier brain-stroke brain-stroke-prediction The Dataset Stroke Prediction is taken in Kaggle. Currently, there is no effective method to predict a stroke using warning signs and hereditary factors. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate This project aims to predict the likelihood of stroke using a dataset from Kaggle that contains various health-related attributes. Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. [ ] Nov 1, 2022 · The dataset is highly unbalanced with respect to the occurrence of stroke events; most of the records in the EHR dataset belong to cases that have not suffered from stroke. : Pathophysiology and treatment of stroke: present status and future perspectives. Stages of the proposed intelligent stroke prediction framework. 2 million new strokes each year [1]. 2 and May 24, 2024 · The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the Câmara J. The random forest classifier provided the highest accuracy among the models for detecting brain stroke. Discussion. In this work, we compare different methods with our approach for stroke Apr 18, 2023 · A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Mar 4, 2022 · Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate the prediction process for the early detection of symptoms related to stroke so The quality of the Framingham cardiovascular study dataset makes it one of the most used data for identifying risk factors and stroke prediction after the Cardiovascular Heart Disease (CHS) dataset . This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. There are a total of 4981 samples. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. The accuracy percentage of the models used in this investigation is significantly higher than that Feb 20, 2018 · Recently, efforts for creating large-scale stroke neuroimaging datasets across all time points since stroke onset have emerged and offer a promising approach to achieve a better understanding of Nov 22, 2024 · Stroke is a serious medical condition that can result in death as it causes a sudden loss of blood supply to large portions of brain. Ischemic Stroke, transient ischemic attack. Kaggle. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Accessed: 2022-07-25. Saritha et al. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. In addition, three models for predicting the outcomes have been developed. 2021. May 26, 2023 · In this paper, three modules were designed and developed for heart disease and brain stroke prediction. The dataset contains information from a sample of individuals, including both stroke and non-stroke cases. Stroke, a leading neurological disorder worldwide, is responsible for over 12. The dataset included 401 cases of healthy individuals and 262 cases of stroke patients admitted in hospital Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. The paper evaluates the reliability of different imaging modalities and their potential contribution to developing robust prediction models. After a stroke, the affected brain areas fail to function normally, making early detection of warning signs crucial for effective treatment and reducing disease severity. When the supply of blood and other nutrients to the brain is interrupted, symptoms May 12, 2021 · We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction Keywords: brain stroke, deep learning, machine learning, classification, segmentation, object detection. Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. Feb 21, 2025 · As a leading cause of death, strokes have been regarded as a dangerously impactful condition with little to no predictability. Atrial fibrillation can result in stroke, which has the potential to be fatal. May 20, 2024 · The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the data used in this study 38,39. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Prediction of brain stroke based on imbalanced dataset in two machine learning algorithms, XGBoost and Neural Network. This dataset comprises 4,981 records, with a distribution of 58% females and 42% males, covering age ranges from 8 months to 82 years. Jan 14, 2025 · To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. In the following subsections, we explain each stage in detail. 1. The dataset’s objective is to estimate the probability of stroke occurring in patients using various input parameters. Project Overview This project focuses on detecting brain strokes using machine learning techniques, specifically a Convolutional Neural Network (CNN) algorithm. Nov 26, 2021 · The dataset used in the development of the method was the open-access Stroke Prediction dataset. Publication: 2019 IEEE International Symposium on Biomedical Dec 28, 2024 · This retrospective observational study aimed to analyze stroke prediction in patients. 95688. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. 61% on the Kaggle brain stroke dataset. , measures of brain structure) of long-term stroke recovery following rehabilitation. Brain stroke prediction is a critical task in healthcare, having the capacity to greatly enhance patient outcomes via early identification and intervention. et al. [6] labeled The title is "Automated Detection and Classification of Ischemic Stroke using Convolutional Neural Networks" Writers: characteristics,Thompson L. Six machine learning classifiers: Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM In this work, we aimed to predict the incidence of strokes using machine learning approaches. 9. Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. The stroke prediction dataset was used to perform the study. { brain-stroke-prediction_dataset, title Jul 7, 2023 · Our dataset, in contrast to most others, concentrates on characteristics that would be significant risk factors for a brain stroke. 6 Module Description: The brain stroke prediction module using machine learning aims to predict the likelihood of a stroke based on input data. Nov 26, 2021 · 2. In this paper, we present an advanced stroke detection algorithm May 27, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. In this study, We evaluate the effectiveness of four cutting-edge algorithms: Convolution-Based Neural Brain Stroke Prediction Using Machine Learning Approach DR. Machine learning (ML) algorithms emerges as a powerful tool for predicting Jan 23, 2022 · The objective of this research is to apply three current Deep Learning (DL) approaches for 6-month IS outcome predictions, using the openly accessible International Stroke Trial (IST) dataset. 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. Dec 9, 2021 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research. This paper proposes a model to achieve an accurate brain stroke forecast. Apr 29, 2024 · Although cardiac stroke prediction has received a lot of attention, brain stroke risk has received comparatively little attention. 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: Random forest Decision tree Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. The best-performing model is deployed in a web-based application, with future developments including real-time data integration. The dataset is in comma separated values (CSV) format, including 4. However, our proposed model, named ENSNET, provides 98. We aim to identify the factors that con In ischemic stroke lesion analysis, Praveen et al. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. Python is used for the frontend and MySQL for the backend. The complex stroke prediction. INTRODUCTION Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health most of the datasets, our dataset focuses on attributes that would have a major risk factors of a Brain Stroke. In theSection 2, we review some literature about ML and brain stroke field whereas, Section 3 presents the study design and selection, search strategy, and categorization of the Nov 19, 2023 · By employing extended datasets of images to train the model, the accuracy of the model for brain stroke prediction can be further improved. Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. Dataset: Stroke Prediction Dataset Dec 16, 2022 · Leveraging a comprehensive dataset, the proposed approach demonstrates superior stroke prediction accuracy compared to individual classifiers, underscoring its potential as an effective tool for Oct 4, 2024 · Stroke prediction dataset, available online: (2022). 1-3 Deprivation of cells from oxygen and other nutrients during a stroke leads to the death of Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. Early diagnosis of brain stroke can help to prevent its adverse effects. We developed a quantitative method to predict strokes before happening. Introduction. In recent years, some DL algorithms have approached human levels of performance in object recognition . Stroke Jun 9, 2021 · This research article aims apply Data Analytics and use Machine Learning to create a model capable of predicting Stroke outcome based on an unbalanced dataset containing information about 5110 stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. There were 5110 rows and 12 columns in this dataset. Oct 1, 2024 · 1 INTRODUCTION. One of the greatest strengths of ML is its Nov 22, 2024 · 2. Abstract. Jul 1, 2023 · 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. The evaluation used 25-fold cross-validation and metrics like accuracy, precision, recall, F1 score, and AUC to assess consistency and generalization, identifying the most effective algorithm The Jupyter notebook notebook. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. 23050. stroke To assemble a varied dataset of brain imaging scans withdiagnosis. proposed a stacked sparse autoencoder (SSAE) architecture for accurate segmentation of ischemic lesions from MR images and performed perfectly on the publicly available Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset, with an average precision of 0. Bentley, P. Keywords - Machine learning, Brain Stroke. To develop the first module, which involves predicting heart disease, machine learning models were trained and tested using structured patient information such as age, gender, and hypertension history, as well as real-time clinical data like heart rate and blood pressure. No records were removed because the dataset had a small subset of missing values and records logged as unknown. Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. 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. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. We used MRI scan data obtained from OpenNeuro, specifically images showing the signs of pre A stroke is a medical emergency when blood circulation in the brain is disrupted or outflowing due to a burst of nerve tissue. The dataset includes demographic and health-related variables such as age, gender, heart disease, hypertension, and smoking status. This research investigates the application of robust machine learning (ML) algorithms, including Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. Since the dataset is small, the training of the entire neural network would not provide good results so the concept of Transfer Learning is used to train the model to get more accurate resul Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. The model was evaluated using two datasets: BrSCTHD-2023 and the Kaggle brain stroke dataset. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. Google Scholar Ozaltin O, Coskun O, Yeniay O, Subasi A (2022) A deep learning approach for detecting stroke from brain CT images using OzNet. It standardizes the brain stroke dataset and evaluates the performance of different classifiers. The key components of the approaches used and results obtained are that among the five different classification algorithms used Naïve Bayes Dec 15, 2022 · State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. csv was read into Data Extraction. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% Jun 1, 2024 · 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'. Nov 21, 2023 · This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. ˛e proposed model achieves an accuracy of 95. 3. Each row in the data provides relavant information about the patient. It is used to predict whether a patient is likely to get stroke based on the input parameters like age, various diseases, bmi, average glucose level and smoking status. predicting brain strokes using the Healthcare Dataset Stroke Data. Machine learning (ML) techniques have been extensively used in the healthcare industry to build predictive models for various medical conditions, including brain stroke, heart stroke and diabetes disease. Why Choose This Dataset? The Stroke Prediction Dataset provides essential data that can be utilized to predict stroke risk, improve healthcare outcomes, and foster research in cardiovascular health. With this thought, various machine learning models are built to predict the possibility of stroke in the brain. Prediction of stroke thrombolysis outcome using CT brain machine learning. ; 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 positive stroke Dec 2, 2024 · According to the World Health Organization (WHO), stroke is a leading cause of death and disability worldwide. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). 1,2 Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Jan 14, 2025 · 3. Similarly, the federated model demonstrates high accuracy while effectively minimizing loss. I. In this research work, with the aid of machine learning (ML This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. Aug 20, 2024 · This study focuses on the intricate connection between general health, blood pressure, and the occurrence of brain strokes through machine learning algorithms. healthcare-dataset-stroke-data. Keywords – Computer learning, brain damage. Created by tarun. This video showcases the functionality of the Tkinter-based GUI interface for uploading CT scan images and receiving predictions on whether the image indicates a brain stroke or not. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. Domain Conception In this stage, the stroke prediction problem is studied, i. Dataset. This paper is based on predicting the occurrence of a brain stroke using Machine Learning. Using the “Stroke Prediction Dataset” available on Kaggle, our primary goal for this project is to delve deeper into the risk factors associated with stroke. Stroke Prediction and Analysis with Machine Learning - nurahmadi/Stroke-prediction-with-ML. Utilizing a dataset from Kaggle, we aim to identify significant factors that contribute to the likelihood of brain stroke occurrence. Feature Selection: The web app allows users to select and analyze specific features from the dataset. Kaggle is an AirBnB for Data Scientists. 13140/RG. Ivanov et al. About. To the best of our knowledge there is no detailed review about the application of ML for brain stroke. Additionally, it attained an accuracy of 96. Jul 4, 2024 · 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. Having a high-quality data collection and cleaning process can streamline the prediction process and help improve the accuracy of predicting brain stroke. One can roughly classify strokes into two main types: Ischemic stroke, which is due to lack of blood flow, and hemorrhagic stroke, due to bleeding. Oct 1, 2023 · 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. It is estimated that the global cost of stroke is exceeding US$ 721 billion and it remains the second-leading cause of death and the third-leading cause of death and disability combined [1]. Transient ischemia attack, ischemic stroke. 28% for brain stroke prediction on the selected dataset. Dataset The dataset used in this project contains information about various health parameters of individuals, including: Sep 1, 2024 · B. It’s a crowd- sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science, machine learning and predictive analytics problems. This dataset was created by fedesoriano and it was last updated 9 months ago. 3. Fig. 1 Brain stroke prediction dataset. Algorithms are compared to select the best for stroke prediction. Jun 17, 2023 · Research in brain stroke prediction is very crucial as it can lead to the development of early detection techniques and interventions that can enhance the prognosis for stroke victims. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. The publisher of the dataset has ensured that the ethical requirements related to this data are ensured to the highest standards. INTRODUCTION Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health Jan 20, 2023 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. The leading causes of death from stroke globally will rise to 6. The "Stroke Prediction Dataset" collected from Kaggle was used to train the models. Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. Deep learning (DL) contributes to stroke treatment by detecting infarcts or hemorrhages, segmenting images, identifying large vessel occlusions, early detection, and providing Only BMI-Attribute had NULL values ; Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median. The application achieved an accuracy of 98. Therefore, the aim of biomarkers associated with stroke prediction. Contribute to Cvssvay/Brain_Stroke_Prediction_Analysis development by creating an account on GitHub. INTRODUCTION. OK, Got it. First, in the pre-processing stage, they used two dimensional (2D) discrete wavelet transform (DWT) for brain images. This paper describes a thorough investigation of stroke prediction using various machine learning methods. Apr 27, 2023 · The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. This dataset has been used to predict stroke with 566 different model algorithms. Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. The output attribute is a Jun 25, 2020 · Authors of [12] tested various models on the dataset provided by Kaggle for stroke prediction. Feb 7, 2024 · 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. Implementation of the study: "The Use of Deep Learning to Predict Stroke Patient Mortality" by Cheon et al. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. 6%, while Logistic Regression and Naïve Bayes had somewhat lower accuracy but were still promising for stroke prediction. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. g. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. Brain Stroke Prediction- Project on predicting brain stroke on an imbalanced dataset with various ML Algorithms and DL to find the optimal model and use for medical applications. Future Direction: Incorporate additional types of data, such as patient medical history, genetic information, and clinical reports, to enhance the predictive accuracy and reliability of the model. Article Google Scholar Nov 8, 2024 · One of the major subclasses of CVDs is stroke, a medical condition in which poor blood flow to the brain causes cell death and makes the brain stop functioning properly. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction has been carried out on the prediction of heart stroke but very few works show the risk of a brain stroke. J. Our dataset, in contrast to most others, concentrates on characteristics that would be significant risk factors for a brain stroke. KADAM1, PRIYANKA AGARWAL2, Dataset named ‘Stroke Prediction Dataset’ from Kaggle: Jan 26, 2021 · 11 clinical features for predicting stroke events. Very less works have been performed on Brain stroke. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. The severity for a stroke can be reduced by detecting it early on. Jan 1, 2024 · To this day, acute ischemic stroke (AIS) is one of the leading causes of morbidity and disability worldwide with over 12. To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in 2456 open source stroke-normal images plus a pre-trained brain stroke prediction model and API. Our study focuses on predicting application of ML-based methods in brain stroke. Nov 18, 2024 · Among all the datasets, missing values has been spotted in the brain stroke dataset only. Stacking. , brain tumors, subdural hematomas) and to determine the type of stroke, its location and the extent of . Bioengineering 9(12):783. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. Our ML model uses a dataset for survival prediction to determine a patient's likelihood of suffering a stroke based on inputs including gender, age, various illnesses, and smoking status. Dataset can be downloaded from the Kaggle stroke dataset. Predict whether you'll get stroke or not !! Detection (Prediction) of the possibility of a stroke in a person. Explainable AI (XAI) can explain the Dec 1, 2024 · After studying the above literature review, most of the researcher’s accuracy was near 95% for brain stroke prediction using brain computed tomography images. This study uses Kaggle's stroke prediction dataset. Machine learning for brain stroke: A review. December, 2022, doi: 10. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e. A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. 2. 49% and can be used for early Nov 21, 2024 · The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. Jan 7, 2024 · Firstly, I’ve downloaded the Brain Stroke Prediction dataset from Kaggle, which you can easily do by going to the datasets section on Kaggle’s website and googling Brain Stroke Prediction. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Dec 23, 2022 · Stroke is the most prevalent illness recognized in the medical community and is on the rise every year. Diagnosis at the proper time is crucial to saving lives through immediate treatment. A strong prediction framework must be developed to identify a person's risk for stroke. Timely prediction and prevention are key to reducing its burden. Users may find it challenging to comprehend and interpret the results. Lesion location and lesion overlap with extant brain Personalized Medicine: The dataset can help develop tools for personalized stroke risk assessments based on individual patient profiles. Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. 18 Jun 2021. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. 86% accuracy for successfully forecasting brain stroke from CT scan images. The Brain stroke prediction model is trained on a public dataset provided by the Kaggle . Recognizing the challenges faced by stroke survivors, the focus shifts to the crucial role of rehabilitation and lifestyle changes in optimizing recovery and quality of life. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing 11 clinical features for predicting stroke events Stroke Prediction Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. We employ multiple machine learning and deep learning models, including Logistic Regression, Random Forest, and Keras Sequential models, to improve the prediction accuracy. Early recognition and detection of symptoms can aid in the rapid treatment of Jul 24, 2024 · Support Vector Machine also performed well at 92. AMOL K. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. Deployment and API: The stroke prediction model is deployed as an easy-to-use API, allowing users to input relevant health data and obtain real-time stroke risk predictions. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. May 15, 2024 · Brain stroke detection using deep convolutional neural network (CNN) models such as VGG16, ResNet50, and DenseNet121 is successfully accomplished by presenting a framework and fundamental principles. csv at master · fmspecial/Stroke_Prediction Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. Given the rising prevalence of strokes, it is critical to understand the many factors that contribute to these occurrences. Feb 7, 2025 · The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. Jul 2, 2024 · We evaluated various machine learning models for stroke prediction on a clinical dataset of 500 CT brain scans, comparing results with actual diagnoses. Sambana, Brain Stroke Prediction by Using Machine Learning - A Mini Project Brain Stroke Prediction by Using Machine Learning in Department of Computer Science & Engineering Lendi Institute of Engineering & Technology, no. , Mawji A. e. References Kuriakose, D. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. , and Sharif M. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Stroke Prediction Dataset|中风预测数据集|医疗健康数据集 收藏 Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. Predicting brain strokes using machine learning techniques with health data. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Jan 10, 2025 · Brain stroke CT image dataset. The experiments used five different classifiers, NB, SVM, RF, Adaboost, and XGBoost, and three feature selection methods for brain stroke prediction, MI, PC, and FI. The API can be integrated seamlessly into existing healthcare systems, facilitating convenient and efficient stroke risk assessment. This project investigates the potential relationship between work status, hypertension, glucose levels, and the incidence of brain strokes. In this paper, we attempt to bridge this gap by providing a systematic analysis of the various patient records for the purpose of stroke prediction. 2 million new cases each year. The value of the output column stroke is either 1 or 0. 55% using the RF classifier for the stroke prediction dataset. In This study describes an integrated approach using optimal selection and allocation methods to predict stroke. Stroke-GFCN: segmentation of Ischemic brain lesions. , computed tomography (CT) scan or magnetic resonance imaging (MRI)) in order to rule out other stroke mimics (e. We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. This dataset consists of 5110 instances and encompasses 12 attributes. It gives users a quick understanding of the dataset's structure. DATA SCIENCE PROJECT ON STROKE PREDICTION- deployment link below 👇⬇️. The effectiveness of several machine learning (ML We would like to show you a description here but the site won’t allow us. ipynb as a Pandas DataFrame; Columns where the BMI value was "NaN" were dropped from the DataFrame 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 systematically This project predicts stroke disease using three ML algorithms - Stroke_Prediction/Stroke_dataset. Ten machine learning classifiers have been considered to predict stroke Effective stroke prevention and management depend on early identification of stroke risk. . Figures 10 and 11 illustrate the performance of our federated model in generalizing across data from different hospitals (5 hospitals) for the Brain Stroke CT Image Dataset both on local and global levels. RELEVANT WORK The majority of strokes are seen as ischemic stroke and hemorrhagic stroke and are shown in Fig. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. The database is biased toward the negative class. May 1, 2024 · This study proposed a hybrid system for brain stroke prediction (HSBSP) using data from the Stroke Prediction Dataset. A. 2. of all fatalities. 55% with layer normalization. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. The dataset used to predict stroke is a dataset from Kaggle. stroke mostly include the ones on Heart stroke prediction. These datasets typically include demographic information, medical histories, lifestyle factors and biomarker data from individuals, allowing ML algorithms to uncover complex patterns and interactions among risk factors. 22% without layer normalization and 94. Nov 9, 2024 · The prediction of brain stroke is based on the Kaggle dataset accessed in September 2024. , ischemic or hemorrhagic stroke [1]. Implementing a combination of statistical and machine-learning techniques, we explored how most of the datasets, our dataset focuses on attributes that would have a major risk factors of a Brain Stroke. integrated wavelet entropy-based spider web plots and probabilistic neural networks to classify brain MRI, which were normal brain, stroke, degenerative disease, infectious disease, and brain tumor in their study. </sec><sec> Methods Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. 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 receiving oxygen and Jun 16, 2022 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. In this paper, authors have proposed an artificial intelligence-based model for the early prediction of brain stroke. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Explore and run machine learning code with Kaggle Notebooks | Using data from Brain stroke prediction dataset Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. where P k, c is the prediction or probability of k-th model in class c, where c = {S t r o k e, N o n − S t r o k e}. , Xiao, Z. Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. A dataset from Kaggle is used, and data preprocessing is applied to balance the dataset. This research aims to use neural network (NN) and machine learning (ML) techniques to assess the probability of a stroke in the brain occurring Libraries Used: Pandas, Scitkitlearn, Keras, Tensorflow, MatPlotLib, Seaborn, and NumPy DataSet Description: The Kaggle stroke prediction dataset contains over 5 thousand samples with 11 total features (3 continuous) including age, BMI, average glucose level, and more. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Brain stroke prediction dataset. 3: Sample CT images a) ischemic stroke b) hemorrhagic stroke c) normal II. 1 Cerebral Stroke Prediction Dataset (CSP) In this study, the CSP dataset sourced from Kaggle was utilized to predict stroke disease. The data pre-processing techniques inoculated in the proposed model are replacement of the missing Many such stroke prediction models have emerged over the recent years. Oct 1, 2020 · Stroke diagnosis involves a detailed medical history, a physical and neurological examination, and a brain imaging test (e. Stroke, a leading cause of disability and mortality globally, necessitates early prediction and intervention to mitigate its devastating effects. 1. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. On the BrSCTHD-2023 dataset, the ViT-LSTM model achieved accuracies of 92. Implementation of DeiT (Data-Efficient Image Transformer) for accurate and efficient brain stroke prediction using deep learning techniques. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. Machine learning techniques show good accuracy in predicting the likelihood of a stroke from related factors. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. 2 Experiments for Brain Stroke CT Image Dataset. ipynb contains the model experiments. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. znxtd bgqhsvn ezgpmh eoczv eswhun wji klt qlxzni fllh lxs uldpe tym bidz zwmtazm ilkpe