Heart stroke dataset Whale Optimization Algorithm (WOA) and Crow Search Algorithm(CSA Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. of Clusters Items Ages (in Sum) Sum of maximum heart rate Disease Cluster1 75 49. 2013 to 2015, 3-year average. In our research, we harnessed the potential of the Stroke Prediction Dataset, a data about stroke to people with various vital checks. S. Stroke Prediction Dataset. The model achieved an accuracy of 99. This paper makes use of heart stroke dataset. It’s a Dataset Source: Healthcare Dataset Stroke Data from Kaggle. In this dataset, 5 heart datasets are combined over 11 common features which heart_stroke_prediction_python using Healthcare data to predict stroke Read dataset then pre-processed it along with handing missing values and outlier. Full size image. Dataset Type There are no Dataset Type that match this search. PDF [90 KB] DOC [3 pages] Recommend on Facebook Tweet Share Dataset. The value of the output column stroke is either 1 or 0. The dataset we have considered is a survey conducted by the government. This paper proposes a model to predict the likelihood of an individual experiencing a heart stroke depend on different input attributes such as age, gender, smoking status, work type. Each row represents a patient, and the columns represent various medical attributes. It’s a severe condition and if treated on time we can save one’s life and treat them well. 33% in detecting heart disease, outperforming several state-of-the-art deep learning models. Only 548 patients out of 29,072 in CVD dataset had stroke conditions, whereas 28,524 patients had no occurrence of stroke. About. First, we need to create a training and testing data set. A deep learning model based on a feed-forward multi-layer arti cial neural network was also studied in [13] to predict stroke. Aug. 36% on coronary heart disease dataset, respectively. the dataset. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. Easily download high quality maps of heart disease, stroke, and socioeconomic conditions for use in Heart disease or stroke mortality. Importing the necessary libraries 2. The dataset was taken from the Ministry of National Guards Health Affairs Hospitals, Kingdom of Provides a comprehensive image for cardiovascular diseases & related prevention. Based on 11 input parameters like gender, age, marital status, profession, hypertension tendencies, BMI, glucose, BP, chest pain, existing The cardiovascular disease dataset is an open-source dataset found on Kaggle. As the dataset is highly unbalanced, we observe that most of the observations are colour coded with negative status of healthcare. Dataset can be downloaded from the Kaggle stroke dataset. #58 (num) (the predicted attribute) Complete attribute documentation: 1 id: patient identification number 2 ccf: social security 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 The algorithm is evaluated using a publicly available benchmark dataset. Object Detection Model snap. #10 (trestbps) 5. , Table 8 presents an analysis of previous research in the field of predicting stroke and heart disease. Pressure, flow, aeration, and heart-rate data were collected during trials which included resting breathing, CPAP at varied PEEP settings, breath-holds, and forced predicting heart stroke using the Kaggle dataset. In this Project, 11 clinical features like hypertension,heart disease,glucose level, BMI and so on are obtained for predicting stroke events. We would like to show you a description here but the site won’t allow us. . Hospitals are shown as the number of hospitals per county. 4. Predict whether a patient is likely to get stroke based on the input parameters like gender, age, various 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. A regression imputation and a simple imputation are applied for the missing values in the stroke dataset, respectively. Similar work was explored in [14, 15, 16] for building an intelligent system to predict stroke from patient records. Given a stroke dataset with risk factors {𝑅1,𝑅2,} and a stroke class Attributes of datasets are qualities used by systems to create predictions; for the cardiovascular system, these features include heart rate, gender, age, and more. The dataset used in this project contains information necessary to predict the occurrence of a stroke. These datasets typically include demographic information, medical histories, lifestyle factors stroke mostly include the ones on Heart stroke prediction. #9 (cp) 4. A Comprehensive Dataset for Machine Learning-Based Heart Disease Prediction. #38 (exang) 10. Learn more about bidirectional Unicode characters. Fig 2. August 27, 2024 Sources Print Share. The proposed work predicts the chances stroke dataset successfully. The Dataset Stroke Prediction is taken in Kaggle. Heart stroke is the leading cause of death Healthcare field has a huge amount of data. Heart disease is becoming a global threat to the world due to people’s unhealthy lifestyles, prevalent stroke history, physical inactivity, and current medical background. Data Pre-processing The dataset obtained contains 201 null values in the BMI attribute which needs to be removed. a reliable dataset for stroke prediction was taken from About. Data description. 8 (b) colour codes the patient records based on the status of the stroke. To get more accurate results, it is better to have large dataset of records of patients from different valid hospitals. data about stroke to people with various vital checks. In 2019, the World Health Organisation (WHO) reported that CD is responsible for 17. To do this, we'll use the Stroke Prediction Dataset provided by fedesoriano on Kaggle. Besides the other diseases which may be diagnosed and treated, heart stroke is mostly a quick occurring event with minimal time for response. Kaggle uses cookies from Google to deliver and enhance the quality of its We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. of Points : 102 Between-group Sum of Squares : 20. Dataset for stroke prediction C. The number 0 indicates that no stroke In [6], heart stroke prediction is analysed using various machine learning algorithms and the Receiver Operating Curve (ROC) is obtained for each algorithm. #16 (fbs) 7. Choose the state. AI Nutrient Tracker. These default settings determine which maps visitors to your site will see initially, but they can change the maps using the interactive features. . The dataset used for stroke prediction is very imbalanced. #4 (sex) 3. U. With the support of the Institute for Health Metrics and Evaluation (IHME), they have merged the GWTG-Stroke This data science project aims to predict the likelihood of a patient experiencing a stroke based on various input parameters such as gender, age, presence of diseases, and smoking status. The detailed strategy Explore and run machine learning code with Kaggle Notebooks | Using data from Stroke Prediction Dataset. 03 Positive Cluster2 27 48. Rates are age-standardized. The following table provides an extract of the dataset used in this article. The dataset is obtained from Kaggle and is available for download. It consists of 5110 observations and 12 variables, including sex, age, medical history, work and marital status, residence type, Sailasya and Kumari [19] used Kaggle's stroke dataset to successfully predict stroke performance across a variety of physiological attributes using various machine learning methods after This dataset contains 42 617 non-stroke and 31 962 strokes after balanced the data; 42 617 non-stroke detection and 783 strokes detected before balanced the data. Figure 10 shows the relationship between The dataset consisted of 10 metrics for a total of 43,400 patients. 29, 2024. Quick Maps of Heart Disease, Stroke, and Social Determinants of Health. 5649 Total Sum of Squares : 29. Early recognition For information on spatial smoothing and data suppression methods used for the Atlas of Heart Disease and Stroke, see the Statistical Methods section of these help pages. 4 Pre-Processing of Data In order for the machine learning algorithms to provide accurate results, the data must first be pre-processed. Facebook LinkedIn Twitter Syndicate. This teaching dataset was developed using the longitudinal Framingham heart study as the data source. quick medical care is critical. of Clusters : 2 No. Show hidden characters Heart Disease Dataset (Most comprehensive) Content Heart disease is also known as Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17. of the Framingham cardiovascular study dataset makes it one of the most used data for identifying risk factors and stroke prediction after the Cardiovascular Heart Disease (CHS) dataset . This 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. Most of our healthy bmi sample between 25 and 75 years old is populated by females. Publicly sharing these datasets can aid in the development of Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. In predictive analytics, many studies were Overview What is the Surveillance & Evaluation Guide? The Surveillance and Evaluation Data Resource Guide for Heart Disease and Stroke Prevention Programs is an at-a-glance compilation of data sources useful for heart disease and stroke prevention programs conducting policy or data surveillance and/or evaluation. Data source: National Vital The proposed framework provides the highest accuracy of 99. Find out more about the AHA Accepted Data Repositories. This project analyzes the Heart Disease dataset from the UCI Machine Learning Repository using Python and Jupyter Notebook. The collection includes patient information, medical history, a gene identification illness database, and indication of stroke disease. This guide addresses the broad The American Heart Association has implemented an Open Science Policy. Authors Visualization 3. The presence of these numbers can reduce the model's accuracy. At the bottom of this page, we have guides on how to train a model using the heart datasets below. Kaggle is an AirBnB for Data Scientists. Data Pre-processing The dataset obtained contains 201 null values in the BMI Cardiovascular disease (CD) is a leading cause of mortality worldwide. Heart Disease and Stroke Data. Each row in the dataset represents a patient, and the dataset includes the following attributes: id: Unique identifier heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease; ever_married: "No The map gallery features maps that are being used to meet heart disease and stroke prevention progra Learn More. There can be n number of factors that can lead to strokes and Framingham Heart Study Dataset Download. Machine learning algorithms have been well suited and their flexibility in predicting stroke risk by analyzing large datasets of patient information. 85 Table 2: Chest Pain Type: Asymptomatic No. In particular, the categorical variables are id, gender, hypertension (yes/no), heart disease (yes/no), marital status, work type (children, government job, never worked, private, self The medical institute provides the stroke dataset. #51 (thal) 14. 2. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Other Cardiovascular and Stroke Related Conferences; Sessions OnDemand; Stroke OnDemand; All RNA-seq datasets are processed using version 4 of the exceRpt small RNA-seq pipeline (Rozowsky et al. 48% on heart Statlog, 93. The teaching dataset includes three clinical examination and 20 year follow-up data based on a subset of the original Framingham cohort participants. Models were developed using XGBoost, Logistic Regression (LR), Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. #19 (restecg) 8. It employs NumPy and Pandas for data manipulation and sklearn for dataset splitting to build a Logistic Regression model for Model training. To review, open the file in an editor that reveals hidden Unicode characters. Bed-based BCG Dataset: ref: 40: ECG, BCG, BP: Recordings from adults whilst at rest. Very less works have been performed on Brain stroke. Specifically, this report presents county (or county equivalent) estimates of heart This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. OK, Got it. e value of the output column stroke is either 1 The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the data used in this study 38,39. We will use an 80:20 approach, 80% of the data to the training set and 20% for the final testing. 25% on stroke dataset, 86% on Framingham dataset and 78. with class labels (stroke and no stroke) are termed the leaf nodes. Cardiovascular Health Study (CHS) dataset for predicting stroke in patients. Formats CSV - 22; JSON - 16; RDF - 16; XML - 16; HTML - 8; EXCEL - 5; Organization Types Federal Stroke disease is a cardiovascular disease that when the blood supply to the brain is interrupted, causing a part of the brain to die. The AHA Precision Medicine Platform offers cloud-computing, diverse datasets, data harmonization, and secure workspaces equipped with state of the art analytics tools, such as artificial intelligence, "In order to prevent heart Cardiovascular Disease dataset The dataset consists of 70 000 records of patients data, 11 features + target. This includes prediction algorithms which use "Healthcare stroke dataset" to predict the occurence of ischaemic heart disease. 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. burger rice sprite "Pâté-(720) 0 1000 Dataset for Heart Stroke Prediction 2. As heart stroke prediction is a complex task, there is a need to automate the prediction process to avoid risks associated with it and alert the patient well in advance. the Stroke dataset is pre-processed and on pre-processed dataset machine learning In fact, stroke is also an attribute in the dataset and indicates in each medical record if the patient suffered from a stroke disease or not. Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Before The RF algorithm achieved the following accuracies with different datasets: 95% with the Cardiovascular Disease Dataset (Kaggle) by Bhatt et al. #40 (oldpeak) 11. Data imputation, feature selection, data preprocessing is 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). e stroke prediction dataset [16] was used to perform the study. MIMIC PERform AF Dataset: ref: 35: ECG, resp: Recordings from critically-ill adults categorised as either AF (19 subjects) or normal sinus Data Exploration of Framingham Heart Study Teaching Dataset¶. The results of this research could be further affirmed by using larger real datasets for heart stroke prediction. 9919 images 1 model. ere were 5110 rows and 12 columns in this dataset. 中文 Chinese; Deutsch (German) Español (Spanish) Français (French) Italiano (Italian) 한국어 (Korean) Русский (Russian) Tiẽng Việt (Vietnamese) Format: Select one. Heart weakness and restricted blood flow into the cavities can cause a range of strokes from mild to severe Heart strokes are primary caused due to the fat deposited on artery walls. Insert What's New Here Language: English. Alberto and Rodríguez [9] utilized data analytics and ML to create a model for predicting stroke outcomes based on an unbalanced dataset, including information on 5110 persons with known stroke In this experiment, we implement a process of stroke risk prediction from our dataset using the various machine learning algorithms. Data Card Code (277) Discussion (19) Suggestions (0) About Dataset. Heart disease We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care. 285 Within-group Sum of Squares : 9. There were 5110 rows and 12 columns in this 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. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Object Detection. Presence of these values can degrade the accuracy At present, big data technology is booming, and it plays an extremely important role in all aspects of our lives, from healthcare and financial services to smart city construction and personalized recommendations, big Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. It shows the advantages and disadvantages of various machine learning models as well as how Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. However, Cerebral Vasoregulation in Elderly with Stroke: Respiratory and heart rate monitoring dataset from aeration study: Respiratory and cardiovascular data collected from 20 subjects. 9 million deaths annually, accounting for 32% of all global In this context, the stroke dataset that includes attributes such as age, hypertension, heart disease, average glucose 2 / Procedia Computer Science 00 (2023) 000–000 level, body mass index (BMI), and stroke status (class attribute) are important as it can help researchers gain crucial insights into the factors that increase the Visit the Atlas of Heart Disease and Stroke to create your own local maps using high-quality data on heart disease and stroke, risk factors, social determinants of health, demographics, and proximity to care. 3. The augmented dataset includes age, BMI, average glucose level, heart disease, hypertension, ever-married, and stroke label features. 9 million lives each year which The "Stroke Prediction Dataset" includes health and lifestyle data from patients with a history of stroke. Additionally, the categorical values are encoded into numerical values using the 'LlB' technique, as training can only be done on Stroke is the fifth leading cause of death and disability in the United States according to the American Heart Association. From the above accuracy summary, Logistic Regression, Random Forest, neural network, and KNN models all give high accuracy score of 98%. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. Hybrid models using superior machine learning classifiers should also be implemented and tested for stroke prediction. Dataset. A limitation of this study is that it has utilized five datasets, expanding which could potentially increase the accuracy. The stroke prediction dataset was used to perform the study. Kaggle uses cookies from Google to deliver and enhance Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Fig. Interestingly, the findings align with another previously The cardiovascular study dataset used for our research is based on the third-generation cohort consisting of about 4238 male and female enrolled participants. data are preprocessed using a label encoder and the missing values of the dataset are filled. Tags __ - 32; cardiovascular-disease - 32; county - 15; cardiovascular - 11; counties - 11; heart-disease - 9; heart - 8; diabetes - 7; stroke - 7; achd-dash - 6; Show More Tags. , 2019) and ERCC Heart-Stroke-Prediction. 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. 556 136. Stroke, a cerebrovascular Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Something went wrong and this page crashed! Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. To the prediction of heart disease, a dataset of 1190 observations was collected from the University of California Irvine (UCI) Machine Learning Repository []. 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. This project leverages machine learning to predict the presence of heart disease in patients based on various health parameters. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The paper focused on classifying the stroke dataset using various machine learning algorithms. Learn more Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Cardiovascular Disease dataset. The Stroke is a leading cause of death worldwide, and early identification of individuals at risk can significantly improve outcomes, and help people be cautious and take preventative measures. Data from the UK Sentinel Stroke National Audit Program between 2013 to 2019 were used. sum() OUTPUT: id 0 gender 0 age 0 hypertension 0 heart_disease 0 ever_married 0 work_type 0 Residence Summary of Diagnostics No. 59 Negative Data Info: The Heart Stroke dataset has 11 features and 1 binary output. Deep learning is Discover datasets around the world! Only 14 attributes used: 1. Heart, stroke and vascular disease – also known as cardiovascular disease (CVD) – is a broad term that describes the many different diseases and conditions that affect the heart and blood vessels. #12 (chol) 6. #3 (age) 2. The features include 4 integers, 2 float, and 5 categorical features. #32 (thalach) 9. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 20-Year US Data: Smoothed Heart/Stroke Death Rates by Age, Race, Sex (Age 35+) 20-Year US Data: Smoothed Heart/Stroke Death Rates by Age, Race, Sex (Age 35+) Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Stroke Prediction and Analysis with Machine Learning - nurahmadi/Stroke-prediction-with-ML. 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. To deal with those data, many Dataset for stroke prediction C. isnull(). #44 (ca) 13. Missing Values: We could find that there are 150 missing values in the Training set and 51 missing values in the Test set. Symptoms can range from mild chest pain and shortness of breath to more severe conditions such as heart attack and stroke. A subset of the original train data is taken using the filtering method for Machine The heart stroke dataset screenshot. 1 Heart Disease Prediction Model. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. Check for Missing values # lets check for null values df. The dataset consists of over 5000 5000 individuals and 10 10 different Analyze the Stroke Prediction Dataset to predict stroke risk based on factors like age, gender, heart disease, and smoking status. Build and deploy a stroke prediction model using R Kenneth Paul Nodado 2023-09-22 Controlled vocabulary, supplemented with keywords, was used to search for studies of ML algorithms and coronary heart disease, stroke, heart failure, and cardiac arrhythmias. The data consists of 70,000 patient records (34,979 presenting with cardiovascular disease and 35,021 not presenting with cardiovascular Risk factors for stroke overlap with those for heart conditions such as heart attack and angina, and include high blood pressure, cholesterol and obesity. The data can be viewed by gender and race/ethnicity. County rates are spatially smoothed. In conjunction Heart stroke remains one of the eminent diseases which has a great impact on the mortality rate. The dataset is in comma separated values (CSV) format, including Heart Stroke Prediction Dataset This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Exploring how data from apps and wearables, linked to other health datasets, can inform trajectories of 2. Perfect for machine learning and research. Physicians and specialists and pharmacies are shown as either the physicians per population and pharmacies per Singh and Choudhary in [12] have used decision tree algorithm on Cardiovascular Health Study (CHS) dataset for predicting stroke in patients. In framingham dataset, only 557 patient records showed the risk of CHD out of 3101. 2 Performed Univariate and Bivariate Analysis to draw key insights. #41 (slope) 12. Please note: This notebook uses open access data. Training set has 3859 datapoints and Test set has 1251 datapoints. Coronary heart disease, stroke and The brain is an energy-consuming organ that heavily relies on the heart for energy supply. - ebbeberge/stroke-prediction heart_disease - Records if the patient has a blood pressure, diabetes and heart disease as major risk factors responsible for stroke attack in an individual. Department of Health & Human Services — This dataset documents rates and trends in heart disease and stroke mortality. 853 124. age: About. This paper is based on predicting the occurrence of a brain stroke classification techniques to predict the possibility of a stroke. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. These metrics included patients’ demographic data (gender, age, marital status, type of work and residence type) and health records (hypertension, heart 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 A stroke is caused when blood flow to a part of the brain is stopped abruptly. 4 Proposed Framework Description. The categories of support vector machine and ensemble (bagged) provided 91% accuracy, while an artificial neural network trained with the stochastic gradient A Comprehensive Dataset for Machine Learning-Based Heart Disease Prediction. Every 40 seconds in the US, someone experiences a stroke, and every four minutes, someone One limitation of this research was the size of the dataset used. Stroke is a medical condition that can lead to the death of a person. This dataset contains different attributes such as age, sex, chest pain type, blood pressure, cholesterol level (in mg/dL), blood sugar, and maximum heart rate. Learn more. Download Dataset We have created a unique, linked, dataset specifically for this data challenge. Stroke Prediction and Analysis with Machine Learning Furthermore, looking at the class distribution, both datasets were highly unbalanced in nature. 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; In this Project, 11 clinical features like hypertension,heart disease,glucose level, BMI and so on are obtained for predicting stroke events. 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. Code sequence to set the default map for heart disease or stroke: data-default-dataset="Heart Disease" (or choose "Stroke") Rates and Trends in Heart Disease and Stroke Mortality Among US Adults (35+) by County, Age Group, Race/Ethnicity, and Sex – 2000-2019. The “healthcare-dataset-stroke-data” is a stroke prediction dataset from Kaggle that contains 5110 observations (rows) with 12 attributes (columns). Each row in the dataset provides relavant information about the patient like age, smoking status, gender, heart disease, bmi, work type and in the end whether the patient suffered a stroke. Data Pre-Processing The BMI property in the retrieved dataset has 201 null values, which must be deleted. Provides a comprehensive image for cardiovascular diseases & related prevention. Table 1 shows the predictive variables, known as an independent Most of the high glucose sample is populated by either children or people over 50 years old. 90% on Heart UCI, 96. Several machine learning algorithms have also been proposed to use these risk factors for predicting stroke occurrence [9], [10]. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL We can also use more advanced classification techniques like CNN or Fuzzy neural networks, in predicting more accurate high risk heart stroke patients. A subset of the In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. We are predicting the stroke probability using clinical measurements for a number of patients. The dataset consists of 303 rows and 14 columns. cytazp elbuun wnkl twfwso rbrh woktr ozwvyz mbjx elfjh ezyxkk qkclr xdde vujyvb btfs ysq