Stroke prediction dataset. The dataset is in comma separated .

Stroke prediction dataset Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. 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. The datasets have been collected from Kaggle. csv') data. csv. e stroke prediction dataset [16] was used to perform the study. Link: healthcare-dataset-stroke-data. Flower allows us to implement clients, simulate a server, and provide special simulation capabilities that create instances of FlowerClient only when needed for Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. This dataset consists of 5110 instances and encompasses 12 attributes. Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. 55% using the RF classifier for the stroke prediction dataset. Nov 18, 2024 · The research was carried out using the stroke prediction dataset available on the Kaggle website. An exploratory data analysis (EDA) and various statistical tests performed on a dataset focused on stroke prediction. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. Sep 22, 2023 · About Data Analysis Report. ˛e proposed model achieves an accuracy of 95. #Create two table: stroke people, normal people #At 99% CI, the stroke people bmi is higher than normal people bmi at 0. 11 clinical features for predicting stroke events. The dataset’s objective is to estimate the probability of stroke occurring in patients using various input parameters. A dataset containing all the required fields to build robust AI/ML models to detect Stroke. We systematically Feb 21, 2025 · 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. Objective: Create a machine learning model predicting patients at risk of stroke. The analysis includes linear and logistic regression models, univariate descriptive analysis, ANOVA, and chi-square tests, among others. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. Machine-learning (ML) may provide a solution to this by leveraging existing routine hospital databases to build accurate stroke risk prediction models and identify novel risk factors for stroke. Stroke Prediction Dataset|中风预测数据集|医疗健康数据集 收藏 We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. Jun 14, 2024 · This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. data=pd. Stroke prediction with machine learning methods among older Chinese. The research methodology included (1) dataset 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. Artificial Intell. In recent years, some DL algorithms have approached human levels of performance in object recognition . Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. The output attribute is a This project aims to predict the likelihood of stroke using a dataset from Kaggle that contains various health-related attributes. As a result, early detection is crucial for more effective therapy. The dataset under investigation comprises clinical and Mar 18, 2021 · For this walk-through, we’ll be using the stroke prediction data set, but having already lost a day to trying and tuning different models for this dataset, I will recommend using a random Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. 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 ). The application achieved an accuracy of 98. Input: The dataset; Output: Classification into 0 (no stroke) or 1 (stroke) Steps: Loading the dataset and required packages; Pre-processing data to convert character to numeric and to remove null values; Dividing the dataset into training set and test set; Importing the Logistic Regression classifier and creating its object. The "Stroke Prediction Dataset" includes health and lifestyle data from patients with a history of stroke. Reload to refresh your session. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. The utilization of publicly available datasets, such as the Stroke Prediction Dataset, offers several advantages. 49% and can be used for early The Dataset Stroke Prediction is taken in Kaggle. ere were 5110 rows and 12 columns in this dataset. This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. Kaggle is an AirBnB for Data Scientists. stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. We use prin- Effective stroke prevention and management depend on early identification of stroke risk. May 27, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. The dataset includes demographic and health-related variables such as age, gender, heart disease, hypertension, and smoking status. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. 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 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 May 27, 2022 · This is by far the largest stroke dataset used for developing prediction of post-stroke mortality model using ML (around 0. It consists of 5110 observations and 12 variables Dec 21, 2021 · In this paper, we will consider using a stroke prediction dataset for building a model for 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. The dataset has a total of 5110 rows, with 249 rows indicating the possibility of a stroke and 4861 rows confirming the lack of a stroke. Within this dataset there are 12 attributes with 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. 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. Speci cally, we consider the common problems of data imputation, feature selection, and predic- Many such stroke prediction models have emerged over the recent years. Jul 28, 2021 · We developed prediction models for the number of heatstroke cases using the datasets between 1 June and 30 September between 2015 and 2017 as the training dataset. Fig. Implementing a combination of statistical and machine-learning techniques, we explored how Oct 21, 2024 · Reading CSV files, which have our data. Users may find it challenging to comprehend and interpret the results. For patients with ischemic stroke, early reperfusion with either thrombolysis or endovascular devices is the most Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. Publicly sharing these datasets can aid in the development of Analysis of the Stroke Prediction Dataset provided on Kaggle. read_csv('healthcare-dataset-stroke-data. 77% to 88. In the first step, we will clean the data, the next step is to perform the Exploratory May 8, 2024 · This study explores the role of data mining and machine learning in stroke prediction. , ischemic or hemorrhagic stroke [1]. 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 31, 2024 · The empirical evaluation, conducted on the cerebral stroke prediction dataset from Kaggle—comprising 43,400 medical records with 783 stroke instances—pitted well-established algorithms such as support vector machine, logistic regression, decision tree, random forest, XGBoost, and K-nearest neighbor against one another. Explainable AI (XAI) can explain the 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. Nov 8, 2024 · Abstract. Discussion. 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. head(10) ## Displaying top 10 rows data. Wacharawichanant, “Performance Analysis and Comparison of Cerebral Stroke Prediction Models on Imbalanced Datasets,” 2022 IEEE/ACIS 7th International Conference on Big Data, Cloud Computing, and Data Science (BCD), Aug. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Mar 6, 2024 · Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. You signed out in another tab or window. ( î ì î ï). This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. 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. In the following subsections, we explain each stage in detail. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. emphasise that their findings do not exclude white matter disruption being a key casual mechanism for post-stroke cognitive symptoms. Early identification of stroke is crucial for intervention, requiring reliable models. 82 bmi #Conclusion: Reject the null hypothesis, finding that higher bmi level is likely Aug 2, 2024 · Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. This dataset has been used to predict stroke with 566 different model algorithms. The dataset is in comma separated values (CSV) format, including 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. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks tensor kaggle-dataset stroke-prediction Updated Mar 30, 2022 Python 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). The dataset included 401 cases of healthy individuals and 262 cases of stroke patients admitted in hospital 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 Brain MRI Segmentation and ISLES datasets are critical image datasets for training algorithms to identify and segment brain structures affected by strokes. 1,2 Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke Mar 5, 2024 · These algorithms leverage patterns and relationships within large datasets to create accurate models that can assist in identifying individuals at risk of stroke. The leading causes of death from stroke globally will rise to 6. Receiver operating characteristic curve performance of stroke risk prediction in (a) total population, (b) rural subgroup, (c) urban subgroup. GitHub repository for stroke prediction project. Our task is to examine existing patient records in the training set and use that knowledge to predict whether a patient in the evaluation set is… Stroke Risk Prediction Dataset (Medical AI) – Version 2. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. 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. Machine learning models can leverage patient data to forecast stroke occurrence by analyzing key clinical Feb 11, 2022 · Datasets used to develop stroke risk prediction models may, for example, Wu Y, Fang Y. Jan 9, 2025 · The results ranged from 73. Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. Feb 7, 2025 · This study proposes a novel approach that applies approximate inverse model explanations (AIME) on a stroke dataset to evaluate the factors that precipitate or prevent stroke occurrence. This dataset contains some obvious outliers and noises, such as age and BMI items. No records were removed because the dataset had a small subset of missing values and records logged as unknown. - ajspurr/stroke_prediction Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). Unfortunately, some samples younger 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. A strong prediction framework must be developed to identify a person's risk for stroke. While risk factors such as high blood pressure, diabetes, and smoking are known to increase stroke risk, the prediction of a stroke remains complex. The next stage is data preprocessing and cleaning, including handling missing values, coding categorical variables Stroke is the main cause of long-term disability and death worldwide; it is a terrible medical condition caused by disrupted blood flow to the brain. Phankokkruad and S. We employ multiple machine learning and deep learning models, including Logistic Regression, Random Forest, and Keras Sequential models, to improve the prediction accuracy. 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 . The participants in the study are presentative for Jul 1, 2021 · This study focuses on various techniques to analyse and retrieve the required information from big data in the stroke prediction dataset. 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 Nov 1, 2019 · In this subsection, we will use the stroke dataset to verify the prediction method for missing values in Section 3. To collect features, a Nov 11, 2024 · Ischemic stroke is a major global health problem since it ranks second among the leading causes of death and disability due to cerebrovascular diseases around the world. In conjunction According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. First, it allows for the reproducibility and transparency 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. Resources Jun 1, 2024 · With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. 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]. 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 smoking status. Accurate prediction of stroke is highly valuable for early in-tervention and treatment. Objectives:-Objective 1: To identify which factors have the most influence on stroke prediction Hence, there is a need for more accurate stroke risk prediction models. The project covers data cleaning, visualization, parameter tuning, and explainable AI techniques. Hope et al. stroke prediction within the realm of computational healthcare. TABLE I DATASETS USED IN THE STUDY, NUMBER OF SAMPLES AND FEATURES Dataset Size Features Framingham Heart Mar 23, 2022 · This study's dataset for stroke prediction was . This dataset was created by fedesoriano and it was last updated 9 months ago. Aug 2, 2024 · Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. 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. For a summary of the characteristics of the dataset, see Table 1. 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 improve prediction accuracy for patient language skills, a finding that was also observed in an independent dataset by Zhao et al. Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Healthcare professionals can discover Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. In the following sections, each dataset will be described in further depth. 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 Performing Various Classification Algorithms with GridSearchCV to find the tuned parameters - Akshay672/STROKE_PREDICTION_DATASET Dec 15, 2022 · State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. 21, 25, 29, 30, 32 Although the RF algorithm has a high accuracy of 90 in all studies, the highest accuracy recorded was in the study Oct 28, 2020 · Stroke is a devastating disease and the leading cause of disability in Canada 1. csv at master · fmspecial/Stroke_Prediction In this project, we decide to use “Stroke Prediction Dataset” provided by Fedesoriano from Kaggle. In the context of stroke prediction using the Stroke Prediction Dataset, various machine learning models have been employed. The comparative analysis of machine learning algorithms in stroke prediction aims to assess the performance and effectiveness of different algorithms in predicting the occurrence of Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. [ ] The dataset used for stroke prediction is very imbalanced. 47 - 2. 21227/mxfb Nov 1, 2022 · Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. - ebbeberge/stroke-prediction A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. 2. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. Title: Stroke Prediction Dataset. Apr 22, 2024 · For this project, I chose to explore a stroke prediction dataset which consists of 11 clinical features for predicting stroke events in patients. Dec 9, 2021 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research. … According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. 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. 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'. In the dataset, Jan 26, 2021 · 11 clinical features for predicting stroke events. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. 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 Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. To improve stroke risk prediction models in terms 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. Speci cally, we consider the common problems of data imputation, feature selection, and predic- This paper utilizes two stroke prediction datasets. 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. Project Overview: Dataset predicts stroke likelihood based on patient parameters (gender, age, diseases, smoking). Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. While using such data to train a machine-level model may result in accuracy, other accuracy measures such as precision and recall are inadequate. Stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Med. The API can be integrated seamlessly into existing healthcare systems Nov 9, 2024 · Although this research has demonstrated promising results on the Kaggle dataset for stroke prediction, future work should involve testing the model on multi-center datasets, which include data from various demographics, geographies, and healthcare systems, and longitudinal data that capture patient health metrics over a period of time. obtained from a publicly accessible site [5]. Optimized dataset, applied feature engineering, and implemented various algorithms. Achieved high recall for stroke cases. 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. 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. Impact: 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. You switched accounts on another tab or window. 17 and compared to a variety of other methods on the dataset “Cardiovascular Health Study (CHS)”. Nov 21, 2023 · title = {Stroke Prediction Dataset}, year = {2023} } RIS TY - DATA T1 - Stroke Prediction Dataset AU - Ahmad Hassan PY - 2023 PB - IEEE Dataport UR - 10. Lesion location and lesion overlap with extant brain Nov 13, 2022 · It is a competition on kaggle with stroke Prediction, which is heavily imbalanced. Dec 14, 2023 · Dataset. According to the methods and standards from MONICA 3 [42], the minimum age of stroke-monitoring should be 25. Domain Conception In this stage, the stroke prediction problem is studied, i. This paper describes a thorough investigation of stroke prediction using various machine learning methods. Publicly sharing these datasets can aid in the development of Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. AIME helps explain the behavior of complex or less transparent AI and machine 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]. A. Feb 10, 2021 · M. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Mar 20, 2023 · Building a step wise step Machine Learning Mode. — World Health Organization (WHO) This project targets the Predicting strokes is essential for improving healthcare outcomes and saving lives. Using SQL and Power BI, it aims to identify trends and correlations that can aid in stroke risk prediction, enhancing understanding of health outcomes in different demographics. Among these, the Stroke Prediction Dataset is essential for developing tabular predictive models focused on risk assessment and early warning signs of stroke. e. Dataset: Stroke Prediction Dataset 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. 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. Purpose of dataset: To predict stroke based on other attributes. 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). 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. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing Acute Ischemic Stroke Prediction A machine learning approach for early prediction of acute ischemic strokes in patients based on their medical history. PySpark is used to build a predictive model to analyse the Dec 2, 2024 · A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. This dataset improves upon a previously unique dataset identified in the literature. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. in this set pertains to strok es. The dataset used to predict stroke is a dataset from Kaggle. 1 Cerebral Stroke Prediction Dataset (CSP) In this study, the CSP dataset sourced from Kaggle was utilized to predict stroke disease. 28% for brain stroke prediction on the selected dataset. Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. Year: 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. The dataset is in comma separated Jul 23, 2023 · Business Understanding. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). </sec><sec> Methods Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. An ensemble model called a Graph depicting attributes in the Stroke Prediction dataset (outcome 0: no stroke, outcome 1: stroke). The developed prediction models Aug 29, 2024 · An algorithm for stroke prediction has been developed by Singh et al. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. The dataset we employed is the Stroke Prediction Dataset, which can be accessed through the Kaggle platform. 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 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 Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. The process begins with data collection, followed by data exploration to understand the structure, features and existing challenges. The latest dataset is updated on 2021 with 5111 instances and 12 attributes. info() ## Showing information about datase data. Our study focuses on predicting Oct 1, 2024 · The number of published articles predicting stroke using ML algorithms from 2019 to August 2023. The effectiveness of several machine learning (ML Nov 22, 2024 · 2. - KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. Resources 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. Our methodology comprises two main steps: firstly, we outline a series of preprocessing and cleaning measures to This report presents an analysis aimed at developing and deploying a robust stroke prediction model using R. 5% accuracy, emphasizing the importance of selecting the right algorithm for a specific dataset. 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. The Pearson correlation heatmap , which investigates the linear relationship between all of the features, is depicted in Figure 3. Dec 28, 2024 · This retrospective observational study aimed to analyze stroke prediction in patients. This RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. Ivanov et al. Saved searches Use saved searches to filter your results more quickly 4 days ago · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Sep 13, 2024 · This study aims to develop a stroke risk prediction model using a dataset from Kaggle that includes demographic, clinical and lifestyle factors. 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. Aug 1, 2023 · Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Whether you’re working on machine learning models or health risk analysis, this dataset offers a rich set of features for developing innovative solutions. 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. Given the rising prevalence of strokes, it is critical to understand the many factors that contribute to these occurrences. When determining . Model comparison techniques are employed to determine the best-performing model for stroke prediction. . After the stroke, the damaged area of the brain will not operate normally. e value of the output column stroke is either 1 This project utilizes the Stroke Prediction Dataset from Kaggle, available here. This work intends to predict stroke occurrence using lifestyle, clinical, and demographic factors. 0. The benchmarks section lists all benchmarks using a given dataset or any of its You signed in with another tab or window. Oct 1, 2024 · In 10 studies, the accuracy of the stroke prediction algorithm was above 90%. Int J Jun 16, 2022 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Synthetically generated dataset containing Stroke Prediction metrics. 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 Feb 18, 2025 · Background Digitalization and big health system data open new avenues for targeted prevention and treatment strategies. This paper introduces a benchmarking dataset, PredictStr, specifically developed to enhance stroke prediction. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. This dataset consists of 5110 rows and 12 columns. 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. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Using Machine Learning (ML) methods including AdaBoost, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). One of the greatest strengths of ML is its 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. This is #Hypothesis: people who had stroke is higher in bmi than people who had no stroke. The data . describe() ## Showing data's statistical features 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. 2022, Nov 26, 2021 · Dataset. Stages of the proposed intelligent stroke prediction framework. Stroke is a common cause of mortality among older people. In this research work, with the aid of machine learning (ML Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. - ankitlehra/Stroke-Prediction-Dataset---Exploratory-Data-Analysis In this work, we aimed to predict the incidence of strokes using machine learning approaches. This project predicts stroke disease using three ML algorithms - Stroke_Prediction/Stroke_dataset. 8, 21, 22, 25, 27-32 Among these 10 studies, five recommended the RF algorithm as the most efficient algorithm in stroke prediction. We aimed to develop and validate prediction models for stroke and myocardial infarction (MI) in patients with type 2 diabetes based on routinely collected high-dimensional health insurance claims and compared predictive performance of traditional regression with state-of-the Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. With help of this CSV, we will try to understand the pattern and create our prediction model. evajt paxys bfvgc hrdftb inppp rae uon gqqjvm ydh ahsodfp uybwt kwmzmqv xebnby wvgadhq waa