Stroke dataset mat. 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. stroke dataset successfully. We previously released a large, open-source dataset of stroke T1-weighted MRIs and manually segmented lesion masks (ATLAS v1. Apr 22, 2024 · In conclusion, the analysis of this stroke dataset could prove beneficial in the realm of medicine in order to help mitigate the strokes in high risk patients. Example Mesh & Electrode coordinates To this end, we previously released a public dataset of 304 stroke T1w MRIs and manually segmented lesion masks called the Anatomical Tracings of Lesions After Stroke (ATLAS) v1. Given a stroke dataset with risk factors {𝑅1,𝑅2,…} and a stroke class This project explores the application of graph analytics and algorithms on a stroke dataset. It was designed to delineate the cause/effect relationship between neural output and the biomechanical functions executed in walking. To solve these problems, we establish a large May 19, 2024 · 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 The dataset must consist of electroencephalography (EEG) data of 50-100 stroke patients. Brain Stroke Dataset Classification Prediction. 22% without layer normalization and 94. The publisher of the dataset has ensured that the ethical requirements related to this data are ensured to the highest standards. 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. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Approximately 15 million individuals worldwide experience a Oct 25, 2024 · This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Of course, this means that the same datapoints of patients with a stroke will be included Dec 8, 2020 · Fig. The dataset consisted of 10 metrics for a total of 43,400 patients. Showing projects matching "class:stroke" by subject, page 1. 2: Summary of the dataset. The time after stroke ranged from 1 days to 30 days. Explore and run machine learning code with Kaggle Notebooks | Using data from Stroke Prediction Dataset Dataset Full Name: Medical University of South Carolina Stroke Data: Dataset Acronym: ARRA: Summary: The Medical University of South Carolina Stroke Data (ARRA*) was a NIH funded study conducted in 2011-12. , , 98. 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. Learn more Oct 6, 2020 · The Mridangam Stroke dataset is a collection of 6977 audio examples of individual strokes of the Mridangam in various tonics. The EEG of the patients whose limbs and face are affected by stroke must be recorded. 96). This repository contains the official PyTorch-implementation of our paper Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks. StrokeRehab consists of 3,372 trials of rehabilitation activities performed by 51 stroke-impaired and 20 healthy subjects. May 27, 2022 · This is by far the largest stroke dataset used for developing prediction of post-stroke mortality model using ML (around 0. Purpose of dataset: To predict stroke based on other attributes. 2022. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. 2, N=304) to encourage the development of better segmentation algorithms. 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 The Mridangam Stroke dataset is a collection of 7162 audio examples of individual strokes of the Mridangam in various tonics. m, which corrects each dataset in turn and creates the final data structures EITDATA and EITSETTINGS stored in UCL_Stroke_EIT_Dataset. A comprehensive sEMG dataset recorded at Mayo Hospital Lahore and National University of Sciences & Technology. 2% of total deaths were due to stroke. 2 dataset. To this end, we previously released a public dataset of 304 stroke T1w MRIs and manually segmented lesion masks called the Anatomical Tracings of Lesions After Stroke (ATLAS) v1. The patients may be Jun 14, 2024 · The stroke dataset was str uctured into a dat a fra me using the Pandas library in Python to facilitate comprehensive analysis. Jun 13, 2021 · This is achieved by separating the full dataset into patients with a stroke and patients without a stroke and then drawing with replacement from the stroke = yes class as many times as there are datapoints in the stroke = no class (4700 datapoints). - stoll2882/Stroke-Data-Analysis-CPSC322 Ischemic stroke is a serious disease that endangers human health. The dataset is used for stroke prediction and analysis. Manual segmentation remains the gold standard, but it is time-consuming and requires significant neuroanatomical expertise. 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. Our dataset’s uniqueness lies in its focus on the acute phase of ischemic stroke, with non-informative native CT scans, and includes a baseline "*How this dataset was obtained, and the details of how each feature was measured is deemed \"confidential\" by the author. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. 55% with layer normalization. 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 Apr 25, 2022 · with class labels (stroke and no stroke) are termed the leaf nodes. Cardiovascular Health Study (CHS) dataset for predicting stroke in patients. 55% using the RF classifier for the stroke prediction dataset. A large, curated, open This web page presents a project that analyzes a stroke dataset from Kaggle and uses various methods to predict the risk of stroke based on measurable predictors. See full list on github. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. Chinese Character Stroke Extraction (CCSE) is a benchmark containing two large-scale datasets: Kaiti CCSE (CCSE-Kai) and Handwritten CCSE (CCSE-HW). The slice thickness of NCCT is 5mm. The project covers data cleaning, visualization, parameter tuning, and explainable AI techniques. To obtain patch datasets X1 and X2, we draw random patches and augment them with standard image processing, expanding the training set diversity. 2 dataset 11. A regression imputation and a simple imputation are applied for the missing values in the stroke dataset, respectively. According to the WHO, stroke is the 2nd leading cause of death worldwide. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, and various diseases and smoking status. All participants were View the paper on Scientific Data: A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms, Liew et al. However, non-contrast CTs may Dec 10, 2022 · This dataset includes cases of MRIs in various stages of sub-acute stroke from multiple previous studies 41,42,43,44 to find machine learning solutions to this frequent issue in stroke lesion Dec 28, 2024 · This study analyzed a dataset comprising 663 records from patients hospitalized at Hazrat Rasool Akram Hospital in Tehran, Iran, including 401 healthy individuals and 262 stroke patients. The data for both sub-tasks, SISS and SPES, are pre-processed in a consistent manner to allow easy application of a method to both problems. The dataset consists of 11 clinical features which contribute to stroke occurence. In 2016, 10. Clinically-meaningful benchmark dataset. 22 participants had right hemisphere hemiplegia and 28 participants had left hemisphere hemiplegia. Additionally, it attained an accuracy of 96. 5 million versus < 1000 in previous ML post-stroke mortality prognosis studies and 77,653 as the largest, to the best of our knowledge, for LR model/score-based approach ). It includes raw signals from healthy subjects and stroke patients performing six upper limb gestures, captured with Myo armband following rigorous ethical standards. teknofest 2021 artificial intelligence dataset in health Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. In particular, we release the code for reproducing the CNN-related results in the main paper Image classification dataset for Stroke detection in MRI scans. Year: 2023. Link: healthcare-dataset-stroke-data. Stroke Risk Prediction Dataset – Clinically-Inspired Symptom & Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. To build the dataset, a retrospective study was Stroke is a type of cardiovascular disease, with two types: ischemic and hemorrhagic stroke. There are six different tonics and ten different stroke labels. Dec 14, 2023 · Dataset. Department of Health & Human Services — This dataset documents rates and trends in heart disease and stroke mortality. Datasets are collections of data. For patients with ischemic stroke, early reperfusion with either thrombolysis or endovascular devices is the most . Stroke is a disease that affects the arteries leading to and within the brain. The participants in the study are presentative for Oct 28, 2020 · Stroke is a devastating disease and the leading cause of disability in Canada 1. 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. As a proxy for user control, we also generate a synthetic geometry dataset of random splines, cut into a patch dataset G. 11 ATLAS is the largest dataset of its kind and This project predicts stroke disease using three ML algorithms - Stroke_Prediction/Stroke_dataset. csv. 345 Jun 16, 2022 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. 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 Classification of a Stroke dataset based on given lifestyle values. This is an updated version of the dataset, as the original version 1. Goal to be create a classification accurate enough to predict if an individual is at risk for having stroke. The patients underwent diffusion-weighted MRI (DWI) within 24 hours after taking the CT. 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. Stroke Datasets Datasets are collections of data. Lesion location and lesion overlap with extant brain Open source computer vision datasets and pre-trained models. I have done EDA, visualisation, encoding, scaling and modelling of dataset. Sep 26, 2023 · Stroke is the second leading cause of mortality worldwide. 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. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. The value of the output column stroke is either 1 or 0. 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. The categories of support vector machine and ensemble (bagged) provided 91% accuracy, while an artificial neural network trained with the stochastic gradient Aug 23, 2023 · The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. Then, we briefly represented the dataset and methods in Section 3. The output attribute is a Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. 11 clinical features for predicting stroke events. 2. Apr 3, 2024 · By offering a carefully collected and annotated dataset, we aim to facilitate the development of advanced diagnostic tools, contributing to improved patient care and outcomes in stroke management. The final steps are given in . 52% Dec 12, 2022 · Study Purpose View help for Study Purpose. It is designed for stroke extraction problems. , , 85. data 5, 1–11 (2018). Globally, 3% of the population are affected by subarachnoid hemorrhage… Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. The dataset comprises of 10 different strokes played on Mridangams with 6 different tonic values. Dataset. These metrics included patients’ demographic data (gender, age, marital status, type of work and residence type) and health records (hypertension, heart disease, average glucose level measured after meal, Body Mass Index (BMI), smoking status and experience of stroke). Assume that the following data are from a portion of this study. A deep learning model based on a feed-forward multi-layer arti cial neural network was also studied in [13] to predict stroke. Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI This dataset includes anonymized images, behavioral measures and demographic details from a cohort of individuals from South Carolina with acute stroke. (10 pts) (Use Stroke Dataset) A recent 10-year study conducted by a research team at the Great Falls Medical School was conducted to assess how age, systolic blood pressure, and smoking relate to the risk of strokes. Article CAS Google Scholar Liew, S. Accuracy is the proportion of properly identified cases overall, providing a broad measure of model performance. Feb 20, 2018 · Stroke is the leading cause of disability in adults, affecting more than 15 million people worldwide each year. 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. There were 5110 rows and 12 columns in this dataset. Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires 2. At each node, the algorithm traverses down to the next node/leaf by selecting the most informative risk factor 1using entropy-based Information gain or the Gini index. The fully BIDS-compatible dataset is fully anonymized, allowing public sharing which is vital for education and development of BIDS pipelines that are capable of processing clinical datasets. *** Dataset. Each row in the data provides relavant information about the patient. We conduct a comprehensive case study involving data preprocessing, EDA, graph centrality measures, and various machine learning models. Chastity Benton 03/2022 [ ] spark Gemini keyboard_arrow_down Task: To create a model to determine if a patient is likely to get a stroke Dec 9, 2021 · can perform well on new data. However, there is insufficient data for this task and current report generation methods mainly focusing on chest CT images can hardly apply to stroke diagnosis. In addition, the authors in aim to acquire a stroke dataset from Sugam Multispecialty Hospital, India and classify the type of stroke by using mining and machine learning algorithms. , 96% with the UCI-Repository dataset by Ch Anwar Ul Hassan et al. There are features 11 features related to life and health status: gender, age, hypertension, heart_disease, ever_married,\twork_type, Residence_type,\tavg_glucose_level, bmi, smoking_status, stroke. The stroke prediction dataset was used to perform the study. ) hours of manual effort with high inter-rate reliability (Cohen kappa > 0. Each row in the data provides Stroke Prediction Dataset Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. Oct 15, 2024 · The dataset encompasses diverse patient characteristics pertinent to stroke prognosis. Aug 2, 2024 · Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. csv at master · fmspecial/Stroke_Prediction Dataset details. 6% with the Cardiovascular Disease Dataset by AbdElminaam et al. In the rehabilitation of arm impairment after stroke, quantifying the training dose (number of repetitions) requires differentiating motions with sub-second durations. ATLAS is the largest dataset of its kind and intended to be a resource for the scientific community to develop more accurate lesion segmentation algorithms. Rates and Trends in Heart Disease and Stroke Mortality Among US Adults (35+) by County, Age Group, Race/Ethnicity, and Sex – 2000-2019 recent views U. On the BrSCTHD-2023 dataset, the ViT-LSTM model achieved accuracies of 92. The rest of the paper is arranged as follows: We presented literature review in Section 2. To this end, we introduce a large-scale, multimodal dataset, StrokeRehab, as a new action-recognition benchmark that includes elemental short-duration actions labeled at a high temporal resolution. This simple model combined with a Feb 9, 2025 · The dataset used for this study is the Acute Ischemic stroke Dataset (AISD) , comprising of Non-Contrast-enhanced Computed Tomography (NCCT), and diffusion-weighted MRI (DWI) scans from 398 subjects. Audio content The dataset provides audio examples for each of the strokes. Evaluation metrics are critical for analyzing the performance of categorization models. BioGPS has thousands of datasets available for browsing and which can be The Stroke Prediction Dataset provides crucial insights into factors that can predict the likelihood of a stroke in patients. Publicly sharing these datasets can aid in the development of Dataset Acronym: STRIDE: Summary: The Stroke Initiative for Gait Data Evaluation (STRIDE) is an initiative based at the University of Southern California to create an inter-institutional, public database containing de-identified demographic and kinematic, kinetic, and spatiotemporal measures assessed via gait analysis in individuals post-stroke Jun 21, 2022 · In addition, the authors in aim to acquire a stroke dataset from Sugam Multispecialty Hospital, India and classify the type of stroke by using mining and machine learning algorithms. The categories of support vector machine and ensemble (bagged) provided 91% accuracy, while an artificial neural network trained with the stochastic gradient Mar 13, 2021 · This dataset is used to predict whether a patient is likely to get a stroke based on the input parameters like gender, age, various diseases, and smoking status. Title: Stroke Prediction Dataset. It contains 11 input features and 1 target, stroke. 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 Stroke Predictions Dataset. It is the only national stroke register in the world to collect longitudinal data on the Style 2 contains 241 photos of eclectic materials, like ribbons and beads. et al. The participants included 39 male and 11 female. It is the second leading cause of death and the third leading cause of disability globally. Feb 20, 2018 · The data set, known as Anatomical Tracings of Lesion After Stroke (ATLAS), is now available for download; researchers around the world are already using the scans to develop and test algorithms Manual segmentation remains the gold standard, but it is time-consuming and requires significant neuroanatomical expertise. 15% with the UCI Dataset by Erdoğan and Güney , 88. -L. Kaggle is an AirBnB for Data Scientists. 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 Publicly accessible datasets like the Healthcare Dataset Stroke Data and the CDC Diabetes Health Indicators offer tabular data widely used in predictive modeling for stroke diagnosis and identifying stroke risk correlations. The key to diagnosis consists in localizing and delineating brain lesions. Stroke instances from the dataset. Muh ‘ Ariful Furqon,Nina Fadilah Najwa,Mohamad Zarkasi,Priza Mar 1, 2025 · The model was evaluated using two datasets: BrSCTHD-2023 and the Kaggle brain stroke dataset. Audio content The dataset provides audio examples for The Dataset Stroke Prediction is taken in Kaggle. Ivanov et al. Automatic and intelligent report generation from stroke MRI images plays an important role for both patients and doctors. /resource/make_final_dataset. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. The NCCT scans are obtained less than 24 h from the onset of ischemia symptoms, and have a slice thickness of 5mm. Similar work was explored in [14, 15, 16] for building an intelligent system to predict stroke from patient records. StrokeQD is a large-scale ischemic stroke dataset established by the cooperation of VRIS research team in Qingdao University of Science & Technology，Qilu Hospital of Shandong University (Qingdao) and Qingdao Municipal Hospital. Our dataset's uniqueness lies in its focus on the acute phase of ischemic stroke, with non-informative native CT scans, and includes a baseline model SPES: acute stroke outcome/penumbra estimation >> Automatic segmentation of acute ischemic stroke lesion volumes from multi-spectral MRI sequences for stroke outcome prediction. Objectives:-Objective 1: To identify which factors have the most influence on stroke prediction Chao Li from Xiaomi Group. 61% on the Kaggle brain stroke dataset. StrokeRehab consists of high-quality inertial measurement unit sensor and video data of 51 stroke-impaired patients and 20 healthy subjects Apr 3, 2024 · By offering a carefully collected and annotated dataset, we aim to facilitate the development of advanced diagnostic tools, contributing to improved patient care and outcomes in stroke management. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The dataset is in comma separated values (CSV) format, including Question: 2. 0 presents some silent or wrong annotated tracks. Source: Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks Dataset Description: The clinical audit collects a minimum dataset for stroke patients in England, Wales and Northern Ireland in every acute hospital, and follows the pathway through recovery, rehabilitation, and outcomes at the point of 6 month assessment. * \n", Jan 9, 2025 · The RF algorithm achieved the following accuracies with different datasets: 95% with the Cardiovascular Disease Dataset (Kaggle) by Bhatt et al. com Aug 22, 2023 · A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. The International Stroke Database is dedicated to providing the international stroke research community with access to clinical and research data to accelerate the development and application of advanced neuroinformatic techniques in clinical settings to improve patient management and ultimately outcome. Stroke is a leading cause of death worldwide, and early prediction can aid in effective prevention strategies. Sep 4, 2024 · This dataset was initially presented in the ISBI official challenge “APIS: A Paired CT-MRI Dataset for Ischemic Stroke Segmentation Challenge”. 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 Mar 7, 2014 · The Mridangam Stroke dataset is a collection of 7162 audio examples of individual strokes of the Mridangam in various tonics. The primary aim is to gain insights from the dataset and improve predictive modelling using graph-based approaches. 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. Sci. 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. The dataset has 44 hours of recorded training and labeled using 2700 (approx. StrokeRehab dataset helps to build deep learning models that can different motions with sub-second durations. 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. Our dataset’s uniqueness lies in its focus on the acute phase of ischemic stroke, with non-informative native CT scans, and includes a baseline 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. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Hi all, This is the capstone project on stroke prediction dataset. The audio examples were recorded 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. S. 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. hhcnmhh wqaeaym vrxtu ewedrqwm kpl old wkii rfps azrqkpq dtcy scadccf uir exbika pmtz doaodhm