Stroke prediction dataset. The value of the output column stroke is either 1 or 0.

Stroke prediction dataset ipynb源代码。 运行项目进行评估 克隆存储库。 Oct 1, 2024 · The number of published articles predicting stroke using ML algorithms from 2019 to August 2023. 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. Stroke prediction with machine learning methods among older Chinese. The stroke prediction dataset was used to perform the study. There were 5110 rows and 12 columns in this dataset. 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. The dataset included 401 cases of healthy individuals and 262 cases of stroke patients admitted in hospital stroke prediction. e stroke prediction dataset [16] was used to perform the study. 49% and can be used for early The Dataset Stroke Prediction is taken in Kaggle. Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. 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. to study the inter-dependency of different risk factors of stroke. 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 RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. 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. Int J Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. g. , ischemic or hemorrhagic stroke [1]. Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. 55% using the RF classifier for the stroke prediction dataset. We employ multiple machine learning and deep learning models, including Logistic Regression, Random Forest, and Keras Sequential models, to improve the prediction accuracy. 0 id 5110 non-null int64 . Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. This dataset consists of 5110 rows and 12 columns. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). Discussion. 234). 01, partial η2 = 0. Achieved high recall for stroke cases. Artificial Intell. In conjunction Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. In recent years, some DL algorithms have approached human levels of performance in object recognition . Learn more Whether a person is at risk of a stroke (Binary Classification). AUC area under the curve, LR logistic regression, AdaBoost adaptive boosting classifier, SVM support vector machines, XGBoost extreme gradient boosting, RF random forest, GNB Gaussian naive Bayes, GBM gradient boosting machine, LGBM light gradient May 27, 2022 · This is by far the largest stroke dataset used for developing prediction of post-stroke mortality model using ML (around 0. Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. It consists of 5110 observations and 12 variables This project utilizes the Stroke Prediction Dataset from Kaggle, available here. 293; p = 0. Machine Learning project using Kaggle Stroke Dataset where I perform exploratory data analysis, data preprocessing, classification model training (Logistic Regression, Random Forest, SVM, XGBoost, KNN), hyperparameter tuning, stroke prediction, and model evaluation. - ebbeberge/stroke-prediction Aug 20, 2024 · The contributions of this work are two-fold: first, we introduce a standardized benchmarking of final stroke infarct segmentation algorithms through the ISLES’24 challenge; second, we provide insights into infarct segmentation using multimodal imaging and clinical data strategies by identifying outperforming methods on a finely curated dataset. Kaggle is an AirBnB for Data Scientists. 2. Sep 30, 2023 · In this dataset, I will create a dashboard that can be used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Impact: This report presents an analysis aimed at developing and deploying a robust stroke prediction model using R. This paper introduces a benchmarking dataset, PredictStr, specifically developed to enhance stroke prediction. In this study, we compare the Cox proportional hazards model with a machine learning approach for stroke prediction on the Cardiovascular Health Study (CHS) dataset. # Column Non-Null Count Dtype . An EEG motor imagery dataset for brain 档案结构 healthcare-dataset-stroke-data. In the dataset, Sep 27, 2022 · 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 . e value of the output column stroke is either 1 Feb 11, 2022 · Datasets used to develop stroke risk prediction models may, for example, Wu Y, Fang Y. The primary goal Dec 21, 2021 · In this paper, we will consider using a stroke prediction dataset for building a model for stroke prediction. Objective: Create a machine learning model predicting patients at risk of stroke. This dataset has been used to predict stroke with 566 different model algorithms. Nov 1, 2019 · Most of the existing researches about stroke prediction are concerned with the complete and class balance dataset, but few medical datasets can strictly meet such requirements. Oct 15, 2024 · Machine learning algorithms have shown promise in revolutionizing stroke prediction by analyzing extensive datasets encompassing demographic information, medical histories, and physiological markers like age, blood pressure, and glucose levels [1, 2]. For the incomplete data, a missing value imputation method based on iterative mechanism has shown an acceptable prediction accuracy [14] , [15] . Jan 14, 2025 · Brain stroke prediction serves as a case study to demonstrate the application’s capabilities, which can be extended to address a variety of pathologies, including heart attacks, cancers, osteoporosis, and epilepsy. csv. It is designed for machine learning and deep learning applications in medical AI and predictive healthcare. You switched accounts on another tab or window. 77% to 88. 1 Digital twin data 3. Project Overview: Dataset predicts stroke likelihood based on patient parameters (gender, age, diseases, smoking). 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. Effective stroke prevention and management depend on early identification of stroke risk. Objectives:-Objective 1: To identify which factors have the most influence on stroke prediction Stroke Prediction K-Nearest Neighbors Model. Title: Stroke Prediction Dataset. 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. In this research work, with the aid of machine learning (ML Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. csv ：在Kaggle中找到的中风预测数据集 Stroke Prediction. From 2007 to 2019, there were roughly 18 studies associated with stroke diagnosis in the subject of stroke prediction using machine learning in the ScienceDirect database [4]. Nov 8, 2024 · Abstract. Our methodology comprises two main steps: firstly, we outline a series of preprocessing and cleaning measures to Oct 28, 2020 · DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Early identification of stroke is crucial for intervention, requiring reliable models. May 8, 2024 · This study explores the role of data mining and machine learning in stroke prediction. Nov 27, 2024 · We used TensorFlow Federated Footnote 1 (TFF) for the tabular dataset (Stroke Prediction Dataset) and Flower framework Footnote 2 for the image dataset (Brain Stroke CT Image Dataset). The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. Year: 2023. 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'. We use prin- Oct 4, 2024 · The authors in 22 used the Cardiovascular Health Study dataset to evaluate two stroke prediction methods: the Cox proportional hazards model and a machine learning technique (CHS). Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Speci cally, we consider the common problems of data imputation, feature selection, and predic- 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 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. We use principal component analysis (PCA) to transform the higher dimensional feature space into a lower dimension subspace, and understand the relative importance of each input attributes. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. The goal of using an Ensemble Machine Learning model is to improve the performance of the model by combining the predictive powers of multiple models, which can reduce overfitting and improve May 24, 2024 · The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the data used in this study 38,39. The dataset is in comma separated values (CSV) format, including May 12, 2021 · The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. ere were 5110 rows and 12 columns in this dataset. To address this challenge, we propose a novel meta-learning framework that integrates advanced hybrid resampling techniques, ensemble-based classifiers, and explainable artificial 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. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', 'Work Type The current American Heart Association/American Stroke Association prevention of stroke guidelines recommend use of risk prediction models to optimize screening and interventions. Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. 1 Brain stroke prediction dataset Jan 1, 2024 · Our clinical dataset included the following features: age, gender, wake-up (whether the patient experienced symptoms at waking up), arterial fibrillation (binary), whether the patient was referred from another hospital, National Institutes of Health Stroke Scale (NIHSS) score at presentation, Time-To-Hospital (TTH), whether treated via 2. - rtriders/Stroke-Prediction You signed in with another tab or window. Ivanov et al. A dataset containing all the required fields to build robust AI/ML models to detect Stroke. Information about the model and application. The value of the output column stroke is either 1 or 0. In this project, we decide to use “Stroke Prediction Dataset” provided by Fedesoriano from Kaggle. However, the deployment of these algorithms in clinical settings presents challenges that must An exploratory data analysis (EDA) and various statistical tests performed on a dataset focused on stroke prediction. What have you used this dataset for? How would you describe this dataset? Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. While risk factors such as high blood pressure, diabetes, and smoking are known to increase stroke risk, the prediction of a stroke remains complex. Stroke risk now follows a sigmoidal curve (sharp increase after age 50), reflecting real-world epidemiological trends. 11 clinical features for predicting stroke events Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. We also provide benchmark performance of the state-of-art machine learning algorithms for predicting stroke using electronic health records. A. First, it allows for the reproducibility and transparency Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Stroke Prediction Dataset|中风预测数据集|医疗健康数据集 收藏 Oct 24, 2024 · The model underwent rigorous training and validation on an imbalanced dataset, which encapsulates a multitude of features linked to stroke risk. The dataset under investigation comprises clinical and The dataset for the project has the following columns: id: unique identifier; gender: "Male", "Female" or "Other" age: age of the patient; hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension The dataset used to predict stroke is a dataset from Kaggle. One of the greatest strengths of ML is its stroke prediction within the realm of computational healthcare. Age-Accurate Risk Modeling:. Jul 1, 2021 · This study focuses on various techniques to analyse and retrieve the required information from big data in the stroke prediction dataset. 3. After the stroke, the damaged area of the brain will not operate normally. This dataset improves upon a previously unique dataset identified in the literature. - ankitlehra/Stroke-Prediction-Dataset---Exploratory-Data-Analysis Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. Sep 22, 2023 · About Data Analysis Report. GitHub repository for stroke prediction project. The model built using sklearn's KNN module and uses the default settings. efficient in the decision-making processes of the prediction system, which has been successfully applied in both stroke prediction [1-2] and imbalanced medical datasets [3]. Stroke Risk Prediction Dataset (Medical AI) – Version 2. Dataset. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. 5% accuracy, emphasizing the importance of selecting the right algorithm for a specific dataset. 0 Stroke Risk Prediction Dataset based on Literature | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. 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 ). This is a demonstration for a machine learning model that will give a probability of having a stroke. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% The Stroke Prediction Dataset provides essential data that can be utilized to predict stroke risk, improve healthcare outcomes, and foster research in cardiovascular health. The source code for how the model was trained and constructed can be found here. In the following subsections, we explain each stage in detail. Explainable AI (XAI) can explain the A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. Our study focuses on predicting The "Stroke Prediction Dataset" includes health and lifestyle data from patients with a history of stroke. The dataset is in comma separated Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Med. Purpose of dataset: To predict stroke based on other attributes. 2. To improve stroke risk prediction models in terms stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. As a result, early detection is crucial for more effective therapy. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. You signed out in another tab or window. The analysis includes linear and logistic regression models, univariate descriptive analysis, ANOVA, and chi-square tests, among others. Optimized dataset, applied feature engineering, and implemented various algorithms. 0021, partial η2 = 0. To optimize the model's performance, we employed hybrid sampling techniques to address the dataset's imbalance and utilized Grid Search to meticulously identify the most optimal parameters for our May 23, 2024 · In fact, (1) the average age of stroke patients is much higher than the average age of those who do not suffer from stroke disease, and due to the decreased immunity of the elderly, the risk of suffering from various diseases will be higher; (2) the average blood glucose of stroke patients is higher, and the results of related studies have . Stroke is a common cause of mortality among older people. In the context of stroke prediction using the Stroke Prediction Dataset, various machine learning models have been employed. Stages of the proposed intelligent stroke prediction framework. ipynb ： Stroke Prediction. The percentage likelihood of stroke occurrence (Regression Analysis). Link: healthcare-dataset-stroke-data. Dec 15, 2022 · State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. Predicting strokes is essential for improving healthcare outcomes and saving lives. Dataset: Stroke Prediction Dataset Dec 14, 2023 · Dataset. Flower allows us to implement clients, simulate a server, and provide special simulation capabilities that create instances of FlowerClient only when needed for This project predicts stroke disease using three ML algorithms - Stroke_Prediction/Stroke_dataset. No records were removed because the dataset had a small subset of missing values and records logged as unknown. The latest dataset is updated on 2021 with 5111 instances and 12 attributes. 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 Synthetically generated dataset containing Stroke Prediction metrics. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). Healthcare professionals can discover Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. Summary without Implementation Details# This dataset contains a total of 5110 datapoints, each of them describing a patient, whether they have had a stroke or not, as well as 10 other variables, ranging from gender, age and type of work Feb 1, 2025 · The results of this research could be further affirmed by using larger real datasets for heart stroke prediction. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. 3,4 Beginning in 1991, the original Framingham Stroke Risk Profile (Framingham Stroke) estimated 10-year risk of developing stroke using key risk factors identified Each person’s stroke risk is influenced by a combination of genetic, environmental, and lifestyle factors, which make it difficult to create a one-size-fits-all predictive model. Fig. Dec 2, 2024 · A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. 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. The utilization of publicly available datasets, such as the Stroke Prediction Dataset, offers several advantages. 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. 1. The Brain MRI Segmentation and ISLES datasets are critical image datasets for training algorithms to identify and segment brain structures affected by strokes. ˛e proposed model achieves an accuracy of 95. The dataset we employed is the Stroke Prediction Dataset, which can be accessed through the Kaggle platform. Resources Jan 9, 2025 · The results ranged from 73. The output attribute is a Nov 18, 2024 · The research was carried out using the stroke prediction dataset available on the Kaggle website. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. 1 gender 5110 non-null Nov 1, 2022 · Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. 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. Aug 1, 2023 · Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. We proposed an efficient retinal image representation together with clinical information to capture a comprehensive overview of cardiovascular health, leveraging large multimodal datasets for new medical insights. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. - ajspurr/stroke_prediction Receiver operating characteristic curve performance of stroke risk prediction in (a) total population, (b) rural subgroup, (c) urban subgroup. Machine learning models can leverage patient data to forecast stroke occurrence by analyzing key clinical 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. In the first step, we will clean the data, the next step is to perform the Exploratory Many such stroke prediction models have emerged over the recent years. Jan 26, 2021 · 11 clinical features for predicting stroke events. PySpark is used to build a predictive model to analyse the 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 Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. , hypertension, chest pain) scale with age (see Medical Validity). The participants in the study are presentative for The "Cerebral Stroke Prediction" dataset is a real-world dataset used for the task of predicting the occurrence of cerebral strokes in individuals. </sec><sec> Methods Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. Whether you’re working on machine learning models or health risk analysis, this dataset offers a rich set of features for developing innovative solutions. Nov 26, 2021 · Dataset. csv at master · fmspecial/Stroke_Prediction 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. Users may find it challenging to comprehend and interpret the results. Jun 14, 2024 · This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. Hybrid models using superior machine learning classifiers should also be implemented and tested for stroke prediction. 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. machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks tensor kaggle-dataset stroke-prediction. 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 We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. e. Reload to refresh your session. Among these, the Stroke Prediction Dataset is essential for developing tabular predictive models focused on risk assessment and early warning signs of stroke. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. In this paper, we perform an analysis of patients’ electronic health records to identify the impact of risk factors on stroke prediction. ; Symptom probabilities (e. Updated Mar 30, 2022; Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. Domain Conception In this stage, the stroke prediction problem is studied, i. The project covers data cleaning, visualization, parameter tuning, and explainable AI techniques. Dec 28, 2024 · This retrospective observational study aimed to analyze stroke prediction in patients. The research methodology included (1) dataset This project aims to predict the likelihood of stroke using a dataset from Kaggle that contains various health-related attributes. Each row in the data provides relavant information about the patient. This web page presents a project that analyzes a stroke dataset from Kaggle and uses various machine learning methods to predict the risk of stroke. Accurate prediction of stroke is highly valuable for early in-tervention and treatment. … Acute Ischemic Stroke Prediction A machine learning approach for early prediction of acute ischemic strokes in patients based on their medical history. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. This dataset was created by fedesoriano and it was last updated 9 months ago. imgtljy wurcroj slsyhgo vqx mba zfsgi ezlmt ajcm bfeo bwesaa kbhbv isxy rjq agsggf nebw