Brain stroke dataset. Brain stroke has been the subject of very few studies.

Brain stroke dataset Both cause parts of the brain to stop functioning properly. Aug 2, 2024 · Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. The time after stroke ranged from 1 days to 30 days. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing Prediction of brain stroke based on imbalanced dataset in two machine learning algorithms, XGBoost and Neural Network neural-network xgboost-classifier brain-stroke-prediction Updated Jul 6, 2023 Background & Summary. The rest of the paper is arranged as follows: We presented literature review in Section 2. A regression imputation and a simple imputation are applied for the missing values in the stroke dataset, respectively. Dec 28, 2024 · The aim of this study is to compare these models, exploring their efficacy in predicting stroke. The chapter is arranged as follows: studies in brain stroke detection are detailed in Part 2. Additionally, it attained an accuracy of 96. Publicly sharing these datasets can aid in the development of The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. 55% with layer normalization. This helps improve prediction reliability[4]. 1. The deep learning techniques used in the chapter are described in Part 3. 1 Brain stroke prediction dataset. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires Image classification dataset for Stroke detection in MRI scans Brain Stroke MRI Images | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. This project aims to predict the likelihood of a person having a brain stroke using machine learning techniques. processing method has been used to increase the dataset's flexibility for training and testing the five classifiers. . This paper reviews May 1, 2024 · Output: Brain Stroke Classification Results. This dataset comprises 4,981 records, with a distribution of 58% females and 42% males, covering age ranges from 8 months to 82 years. Jun 16, 2022 · Here we present ATLAS v2. Feb 20, 2018 · A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. [ ] Brain stroke prediction dataset. 30%, which was the highest possible. 3. These antennas are deployed in a fixed circular array around the head, at a distance of approximately 2-3 mm from the head. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. The emergence of deep learning methodologies has transformed the landscape of medical image analysis. Keywords - Machine learning, Brain Stroke. h5 after training. Scientific Data , 2018; 5: 180011 DOI: 10. Step 1: Start Step 2: Import the necessary packages. Large datasets are therefore imperative, as well as fully automated image post- … To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Six realistic head phantom computed from MRI scans, is surrounded by an antenna array of 16 dipole antennas distributed uniformly around the head. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. According to the WHO, stroke is the 2nd leading cause of death worldwide. Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction 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'. Dataset The dataset used in this project contains information about various health parameters of individuals, including: Mar 11, 2025 · The proposed work resolves these challenges and introduces a new model named an Enhanced Reduce Dimensionality Pattern Convolutional Neural Networks (ERDP-CNN) to improve stroke detection accuracy and efficiency in brain CT images. The Cerebral Vasoregulation in Elderly with Stroke dataset provides valuable insights into cerebral blood flow regulation post stroke, useful for both tabular analysis and image-based Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. 1038/sdata. 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. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. Segmentation of the affected brain regions requires a qualified specialist. ipynb contains the model experiments. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. 05 s). We systematically Tags: artery, astrocyte, brain, brain ischemia, cell, cerebral artery occlusion, glutamine, ischemia, middle, middle cerebral artery, protein, stroke, vimentin View Dataset Expression data from reactive astrocytes acutely purified from young adult mouse brains Sep 26, 2023 · Stroke is the second leading cause of mortality worldwide. Using a dataset from Kaggle with labelled CT scans for 2,500 stroke cases and 2,500 non-stroke cases (each image Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Early stroke detection can improve patient survival rates, however, developing nations often lack sufficient medical resources to provide appropriate Nov 19, 2022 · The proposed signals are used for electromagnetic-based stroke classification. Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke patient survival based on a comprehensive set of factors. Large datasets are therefore Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Immediate attention and diagnosis play a crucial role regarding patient prognosis. Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. 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}. Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. This is a serious health issue and the patient having this often requires immediate and intensive treatment. csv at master · fmspecial/Stroke_Prediction Stroke is a disease that affects the arteries leading to and within the brain. , where stroke is the fifth-leading cause of death. In this Project Respectively, We have tried to a predict classification problem in Stroke Dataset by a variety of models to classify Stroke predictions in the context of determining whether anybody is likely to get Stroke based on the input parameters like gender, age and various test results or not We have made the detailed exploratory Jan 1, 2023 · In this chapter, deep learning models are employed for stroke classification using brain CT images. For example, intracranial hemorrhages account for approximately 10% of strokes in the U. Ischemic Stroke, Jul 8, 2024 · An ischemic stroke occurs when a blood clot blocks the flow of blood and oxygen to the brain, while a hemorrhagic stroke happens when a weakened blood artery in the brain ruptures and leaks . Nov 21, 2023 · 12) stroke: 1 if the patient had a stroke or 0 if not *Note: "Unknown" in smoking_status means that the information is unavailable for this patient. However, non-contrast CTs may Nov 18, 2024 · In the brain stroke dataset, the BMI column contains some missing values which could have been filled using either the median or mean of the column. Stacking. This dataset Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. The role and support of trained neural networks for segmentation tasks is considered as one of the best assistants Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. The input variables are both numerical and categorical and will be explained below. Unlike most of the datasets, our dataset focuses on attributes that would have a major risk factors of a Brain Stroke. This research investigates the application of robust machine learning (ML) algorithms, including . The key to diagnosis consists in localizing and delineating brain lesions. Acknowledgements (Confidential Source) - Use only for educational purposes If you use this dataset in your research, please credit the author. Stroke is a major public health concern, with early detection and intervention being crucial for improved outcomes. Brain stroke is one of the global problems today. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% This project predicts stroke disease using three ML algorithms - Stroke_Prediction/Stroke_dataset. Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. However, these early works often faced challenges due to the limited size of available datasets. Over the years, various studies have been conducted to develop reliable methods for detecting brain stroke disease, particularly using machine learning techniques. 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. Moreover, the Brain Stroke CT Image Dataset was used for stroke classification. 0 (n=955), a larger dataset of stroke T1-weighted MRIs and lesion masks that includes both training (public) and test (hidden) data. Feb 20, 2018 · Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. 22% without layer normalization and 94. Step 3: Read the Brain Stroke dataset using the functions available in Pandas library. Here we present ATLAS v2. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. 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. The Brain stroke prediction model is trained on a public dataset provided by the Kaggle . 61% on the Kaggle brain stroke dataset. An image such as a CT scan helps to visually see the whole picture of the brain. Signs and symptoms of a stroke may include an inability to move or feel on one side of the body, problems understanding or speaking, dizziness, or loss of vision to one side. The best-performing model is deployed in a web-based application, with future developments including real-time data integration. g. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Here we present ATLAS (Anatomical Tracings of Lesions Acute ischemic stroke dataset contains 397 Non-Contrast-enhanced CT (NCCT) scans of acute ischemic stroke with the interval from symptom onset to CT less than 24 hours. For hyper-acute strokes, SVM led in accuracy (94. 1,2 Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. Nov 26, 2021 · The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. 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. Feb 20, 2018 · Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. The patients underwent diffusion-weighted MRI (DWI) within 24 hours after taking the CT. Upon comparing the results, the models Aug 23, 2023 · To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Mar 25, 2024 · The Ischemic Stroke Lesion Segmentation (ISLES) dataset serves as an important resource in the field of stroke lesion segmentation. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. Similarly, CT images are a frequently used dataset in stroke. Oct 25, 2024 · This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. python database analysis pandas sqlite3 brain-stroke. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. These Jan 10, 2025 · In , the authors suggested a model with a strategy for predicting brain strokes accurately. 3. Clinical and imaging data may not be homogeneous, long-term functional outcomes may not be assessed, and comorbidities and lifestyle factors may be The OASIS data are distributed to the greater scientific community under the following terms: User will not use the OASIS datasets, either alone or in concert with any other information, to make any effort to identify or contact individuals who are or may be the sources of the information in the dataset. Dec 31, 2024 · The dataset that was used includes 4982 patients' observation problems with 11 brain stroke-related attributes. The participants included 39 male and 11 female. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. Nov 22, 2024 · Brain stroke datasets sometimes have limited and homogenous sample numbers, incomplete or inconsistent data that may add bias, and quick follow-up periods that may not capture long-term results. Brain Stroke Dataset Classification Prediction. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. 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 However, these datasets are limited in terms of sample size; the PhysioNet dataset contains 82 CT scans, while the INSTANCE22 dataset contains 130 CT scans. 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. Figure of Brain Stroke detection flowchart DATASET: Creating a dataset for brain stroke detection using machine learning algorithms is a critical step in developing accurate and reliable models for automated diagnosis. Statistical analysis and visualization techniques are utilized to understand the underlying relationships between features and stroke risk. The dataset consists of over 5000 5000 individuals and 10 10 different input variables that we will use to predict the risk of stroke. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. 11 Cite This Page : Dec 9, 2021 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research. Implementing a combination of statistical and machine-learning techniques, we explored how Mar 29, 2021 · stroke_prediction:根据世界卫生组织（WHO）的数据，卒中是全球第二大死亡原因，约占总死亡人数的11％。该数据集用于根据输入参数（例如性别，年龄，各种疾病和吸烟状况）预测患者是否可能中风。 Oct 1, 2020 · Besides, maximum studies are found in stroke diagnosis although number for stroke treatment is least thus, it identifies a research gap for further investigation. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e. The limited availability of samples in public datasets for brain hemorrhage segmentation is primarily due to the labor-intensive and time-consuming process required for pixel-level annotation. [14] Sook-Lei Liew, Bethany P Lo, Miranda R Donnelly, Artemis Zavaliangos-Petropulu, Jessica N Jeong, Giuseppe Barisano, Alexandre Hutton, Julia P Simon, Julia M Juliano, Anisha Suri, et al. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. drop('id',axis=1) Step 5: Apply MEAN imputation method to impute the missing values. Upload any CT scan image, and the interface will predict whether the image shows signs of a brain stroke. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. Since stroke cases are relatively rare compared to non-stroke cases, deep learning models employ techniques such as oversampling, undersampling, and synthetic data generation to balance datasets. Jan 14, 2025 · 3. Apr 21, 2023 · Analyzed a brain stroke dataset using SQL. Mar 11, 2025 · 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. They concluded that their suggested model had an accuracy of 95. , measures of brain structure) of long-term stroke recovery following rehabilitation. According to the World Health 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. 1. Dec 12, 2022 · Study Purpose View help for Study Purpose. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. All participants were Jan 20, 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 primary contribution of this work is as follows: (1) Explore and compare influences of the different preprocessing techniques for stroke prediction according to machine learning. Dec 7, 2024 · 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. csv", header=0) Step 4: Delete ID Column #data=data. The dataset was obtained from Kaggle and the proposed architectures were Random Forest, Decision Tree, and SVM. The model is saved as stroke_detection_model. Sep 30, 2024 · Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes. 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. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. The project code automatically splits the dataset and trains the model. Updated Feb 12, 2023; Jupyter Notebook; stroke dataset successfully. 9%), closely followed by random forest (92. 2018. In this research work, with the aid of machine learning (ML Mar 1, 2025 · The model was evaluated using two datasets: BrSCTHD-2023 and the Kaggle brain stroke dataset. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. However, manual segmentation requires a lot of time and a good expert. On the BrSCTHD-2023 dataset, the ViT-LSTM model achieved accuracies of 92. Brain stroke has been the subject of very few studies. 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. Exploratory Data Analysis (EDA): EDA techniques are employed to gain insights into the dataset, visualize stroke-related patterns, and identify significant factors contributing to stroke occurrences. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Both variants cause the brain to stop functioning properly. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. 9. Jul 2, 2024 · Table 1’s analysis reveals the performance of various machine learning classifiers on an original brain ischemic stroke dataset before integrating the SPEM model. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. Performance of Deep Learning Models: Apr 3, 2024 · A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI Stroke instances from the dataset. Abstract. Globally, 3% of the population are affected by subarachnoid hemorrhage… Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal 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. Aug 22, 2023 · We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. This study analyzed a dataset comprising 663 records from patients hospitalized at Hazrat Rasool Nov 9, 2024 · Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. However, regarding stratification by lesion complexity , it is important to note that the dataset does not specifically provide manual stratification by lesion complexity in UniToBrain dataset: a Brain Perfusion Dataset Daniele Perlo1[0000−0001−6879−8475], Enzo Tartaglione2[0000−0003−4274−8298], Umberto Gava3[0000 − 0002 9923 9702], Federico D’Agata3, Edwin Benninck4, and Mauro Bergui3[0000−0002−5336−695X] 1 Fondazione Ricerca Molinette Onlus 2 LTCI, T´el´ecom Paris, Institut olytechnique de The model was evaluated using two datasets: BrSCTHD-2023 and the Kaggle brain stroke dataset. To build the dataset, a retrospective study was Age has correlations to bmi, hypertension, heart_disease, avg_gluclose_level, and stroke; All categories have a positive correlation to each other (no negatives) Data is highly unbalanced; Changes of stroke increase as you age, but people, according to this data, generally do not have strokes. Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke outcomes 3–6. a reliable dataset for stroke This work introduced APIS, the first paired public dataset with NCCT and ADC studies of acute ischemic stroke patients. Sep 4, 2024 · This dataset was initially presented in the ISBI official challenge “APIS: A Paired CT-MRI Dataset for Ischemic Stroke Segmentation Challenge”. A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. Using the Tkinter Interface: Run the interface using the provided Tkinter code. Jul 4, 2024 · The Brain MRI Segmentation and ISLES datasets are critical image datasets for training algorithms to identify and segment brain structures affected by strokes. The purpose of the study was to provide high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. A Gaussian pulse covering the bandwidth from 0 Jan 14, 2025 · 3. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. 0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. The Jupyter notebook notebook. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze The Brain MRI Segmentation and ISLES datasets are critical image datasets for training algorithms to identify and segment brain structures affected by strokes. #pd. Scientific data, 5(1):1–11, 2018. Brain stroke prediction dataset. 87 s) being quicker than SVM (53. Then, we briefly represented the dataset and methods in Section 3. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test data allows for unbiased performance evaluation via web-based challenges. Ivanov et al. Handling imbalanced datasets is another critical challenge. The leading causes of death from stroke globally will rise to 6. Recently, Transformers, initially designed for natural language processing, have exhibited remarkable capabilities in various computer Sep 13, 2023 · This data set consists of electroencephalography (EEG) data from 50 (Subject1 – Subject50) participants with acute ischemic stroke aged between 30 and 77 years. In this paper, we present an advanced stroke detection algorithm Feb 21, 2025 · We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the Feb 24, 2025 · The BraTS2020 dataset is widely used in brain tumor segmentation research, particularly for glioma tumors, and it includes a variety of brain tumor types and complexities. Intracranial Hemorrhage is a brain disease that causes bleeding inside the cranium. The 2022 version of ISLES comprises 400 MRI cases sourced from multiple vendors, with 250 publicly accessible cases and 150 private ones [ 67 ] . Feb 20, 2018 · Stroke is the leading cause of disability in adults, affecting more than 15 million people worldwide each year. Finally SVM and Random Forests are efficient techniques used under each category. S. The output attribute is a Contribute to Cvssvay/Brain_Stroke_Prediction_Analysis development by creating an account on GitHub. APIS was presented as a challenge at the 20th IEEE International Symposium on Biomedical Imaging 2023, where researchers were invited to propose new computational strategies that leverage paired data and deal with lesion our ML model uses dataset to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. read_csv("Brain Stroke. 0%), with random forest (41. A USC-led team has compiled and shared one of the largest open-source datasets of brain scans from stroke patients, the NIH-supported Anatomical Tracings of Lesion After Stroke (ATLAS) dataset. To address this challenge, we propose a novel meta-learning framework that integrates advanced hybrid resampling techniques, ensemble-based classifiers, and explainable artificial 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. The Cerebral Vasoregulation in Elderly with Stroke dataset provides valuable insights into cerebral blood flow regulation post stroke, useful for both tabular analysis and image-based The dataset used in the development of the method was the open-access Stroke Prediction dataset. tpj kyqv uqq ykppv rkhs ybmu nvza rubtb wwrxh effnxj qkjqzb orz gzadv tddltfh halu