Heart stroke prediction dataset 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 May 20, 2024 · The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the the imbalanced dataset highlighted hypertension and heart disease as the 4th and 5th most blood pressure, diabetes and heart disease as major risk factors responsible for stroke attack in an individual. Our study focuses on predicting Jun 30, 2022 · A stroke is caused by damage to blood vessels in the brain. One-Hot Encoding for Categorical Variables: Ensures that categorical variables are properly incorporated into the model. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', 'Work Type Jul 1, 2021 · Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. 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. 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. 1 Digital twin data 3. The stroke prediction dataset was used to perform the study. Prediction is done based on the condition of the patient, the ascribe, the diseases he has, and the influences of those diseases that lead to a stroke, early prediction of heart stroke risk can help in timely Intercede to minimize the risk of stroke, by making use of Machine learning algorithms, for 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. 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]. Synthetic Heart Disease Risk Prediction Dataset: A Comprehensive Collection Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Section 3 describes the experimental setup and dataset and explains the methodology. 2. This RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. This project uses Kaggle's Stroke Prediction dataset to predict heart stroke where the classes are not balanced. 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. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). They deployed DT, RF, and a hybrid approach combining both algorithms. The dataset consisted of 10 metrics for a total of 43,400 patients. 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. 3. In the following subsections, we explain each stage in detail. 3. We use machine learning and neural networks in the proposed approach. Nov 1, 2022 · We propose a predictive analytics approach for stroke prediction. Data Pre-processing The dataset obtained contains 201 null values in the BMI attribute which needs to be removed. Stacking. As an optimal solution, the authors used a combination of the Decision Tree with the C4. Oct 4, 2024 · In addition, the authors investigated 20 the use of predictive analytics techniques for stroke prediction using deep learning models applied to heart disease datasets. However, a systematic analysis of the risk factors is missing. isnull(). Section 4 presents the results and outcomes using the various machine learning algorithms, before Section 5 presents a comparative evaluation 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. The "Framingham" heart disease dataset has 15 attributes and over 4,000 records. 2 Performed Univariate and Bivariate Analysis to draw key insights. Feb 5, 2024 · Heart attack is a catch-all term for a variety of conditions affecting the heart. e. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. Oct 7, 2024 · The datasets have many features that can be used for heart disease prediction including age, gender, blood pressure, cholesterol levels, electrocardiogram readings-ECG, chest pain, exercise Apr 1, 2022 · Attempts have been made to identify predictors of recurrent stroke using Cox regression without developing a prediction model. Nov 8, 2023 · About Data Analysis Report. May 8, 2024 · accuracy score of 92. The BRFSS 2015 dataset so far is relatively new and not well experimented till date for classification using different machine learning algorithms. 55% using the RF classifier for the stroke prediction dataset. heart stroke prediction is performed the use of a dataset This dataset documents rates and trends in heart disease and stroke mortality. The dataset included 401 cases of healthy individuals and 262 cases of stroke patients admitted in hospital 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. Each row in the data provides relavant information about the patient. The datasets used are classified in terms of 12 parameters like hypertension, heart disease, BMI, smoking status, etc. With help of this CSV, we will try to understand the pattern and create our prediction model. Information about the model and application. 13,14 Logistic regression was used with only clinical and imaging variables (AUROC, 0. Fig 2. 9. Several studies have been conducted using the Stroke Prediction Dataset in recent years, and the results have been Summary. We systematically Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. This Jan 5, 2024 · This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. The data pre-processing techniques inoculated in the proposed model are replacement of the missing prediction of stroke. The heart disease and brain stroke prediction models were found to be 100% and 97. No records were removed because the dataset had a small subset of missing values and records logged as unknown. data=pd. About. An early detection system for signs of a heart attack must be implemented in light of the alarming rise in the number of heart attacks in Dec 28, 2024 · This retrospective observational study aimed to analyze stroke prediction in patients. 1. To review, open the file in an editor that reveals hidden Unicode characters. Structure. The system proposed in this paper specifies. 1% accurate in predicting heart disease and brain stroke, respectively, based on clinical and patient information, while the MRI image-based deep learning stroke prediction model was 96. The current American Heart Association/American Stroke Association prevention of stroke guidelines recommend use of risk prediction models to optimize screening and interventions. Learn more Our Heart Stroke Prediction project utilizes machine learning algorithms to predict the likelihood of a person having a stroke based on various risk factors. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. As part of the central nervous system, the brain is the organ that controls vision, memory, touch, thought, emotion, breathing, motor skills, hunger, and all other functions that govern our body. 74) whereby performance was measured on the same data used for model development (no separate test data). In the Dec 8, 2020 · Fig. 71), only retinal characteristics (AUROC, 0. 5 algorithm, Principal Component Analysis, Artificial Neural Networks, and Support Vector In this Project Respectively, We have tried to a predict classification problem in Stroke Dataset by a variety of models to classify Stroke predictions in the context of determining whether anybody is likely to get Stroke based on the input parameters like gender, age and various test results or not We have made the detailed exploratory Stroke Prediction K-Nearest Neighbors Model. Age, heart disease, average glucose level are important factors for predicting stroke. In recent years, some DL algorithms have approached human levels of performance in object recognition . Check for Missing values # lets check for null values df. The value of the output column stroke is either 1 or 0. Mar 10, 2023 · In order to predict the heart stroke, an effective heart stroke prediction system (EHSPS) is developed using machine learning algorithms. . In the Heart Stroke dataset, two class is totally imbalanced and heart stroke datapoints will be easy to ignore to compare with the no heart stroke datapoints. The dataset provides relevant information about each patient, enabling the development of a predictive model. Jun 25, 2020 · Authors of [12] tested various models on the dataset provided by Kaggle 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, various diseases, and smoking status. 15 Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. Learn more Contribute to anandj25/Heart-Stroke-Prediction development by creating an account on GitHub. This is a demonstration for a machine learning model that will give a probability of having a stroke. In addition, effect of pre-processing the data has also been summarized. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. By identifying individuals who are at high risk of having a heart stroke, healthcare providers can intervene early to prevent the onset of the condition or minimize its effects [6, 10 Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. 2: Summary of the dataset. To enhance the accuracy of the stroke prediction model, the dataset will be analyzed and processed using various data science methodologies and algorithm About 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. head(10) ## Displaying top 10 rows data. The target of the dataset is to predict the 10-year risk of coronary heart disease (CHD). Learn more We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. 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. Also, the 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 . There were 5110 rows and 12 columns in this dataset. Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. A dataset containing all the required fields to build robust AI/ML models to detect Stroke. In this research work, with the aid of machine learning (ML stroke prediction. 65), and both (AUROC, 0. Each row represents a patient, and the columns represent various medical attributes. 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. Jun 24, 2023 · The heart is one of the most vital organs in our body and crucial for proper bodily function, an unfit heart can seriously affect fitness, lifestyle and severely decrease the expected lifetime of an individual making a healthy heart necessary for survival. 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; Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. This includes prediction algorithms which use "Healthcare stroke dataset" to predict the occurence of ischaemic heart disease. 5110 observations with 12 characteristics make up the data. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. 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]. Ivanov et al. - ajspurr/stroke_prediction has been carried out on the prediction of heart stroke but very few works show the risk of a brain stroke. - ebbeberge/stroke-prediction Apr 16, 2023 · It is necessary to automate the heart stroke prediction procedure because it is a hard task to reduce risks and warn the patient well in advance. In raw data various information such as person's id ,gender ,age ,hypertension ,heart_disease ,ever_married, work_type, Residence_type ,avg_glucose_level, bmi ,smoking_status ,stroke are given. Recall is very useful when you have to According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Therefore, the stroke must be precisely predicted to begin treatment as soon as possible. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. 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. stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. - akshit113/Heart-Stroke-Prediction Nov 24, 2023 · This project uses Kaggle's Stroke Prediction dataset to predict heart stroke where the classes are not balanced, and it has been observed that the Instance Hardness Threshold re-sampling technique along with the Exhaustive feature selection method across the Random Forest classifier yields a better accuracy. Specifically, this report presents county (or county equivalent) estimates of heart Synthetically generated dataset containing Stroke Prediction metrics. As a limitation, there could be more advanced initial centroid selection methods in future which will be directly incorporated in K-means Clustering algorithm. Prediction of brain stroke using clinical attributes is prone to errors and takes A Comprehensive Dataset for Machine Learning-Based Heart Disease Prediction Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. We are predicting the stroke probability using clinical measurements for a number of patients. 1 Brain stroke prediction dataset Jan 1, 2022 · The pattern of the attributes as per the provided dataset was monitored for accurate prediction of heart stroke in the patients. In the proposed model, heart stroke prediction is performed on a dataset collected from Kaggle. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. Nov 1, 2023 · The use of machine learning algorithms in heart stroke prediction has the potential to significantly improve patient outcomes and reduce healthcare costs. Project Thesis This project employs machine learning principles on extensive existing datasets to predict stroke risk based on Jan 4, 2024 · In a study conducted by 25, the researchers utilized the Cleveland heart disease dataset to perform heart disease prediction. 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 Feb 1, 2025 · Section 2 briefly introduces some related work on machine learning-based heart stroke detection and prediction. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. L. These metrics included patients’ demographic data (gender, age, marital status, type of work and residence type) and health records (hypertension, heart disease, average glucose level measured after meal, Body Mass Index (BMI), smoking status and experience of stroke). teenagers. Research Drive. Stroke Prediction Dataset Sep 22, 2023 · About Data Analysis Report. It is one of the major causes of mortality worldwide. Jan 5, 2024 · This multifaceted approach holds the potential to significantly impact the field of healthcare by offering a reliable and understandable tool for heart stroke prediction. In this research article, machine learning models are applied on well known heart stroke classification data-set. Stroke prediction is a tough paintings that necessitates a large quantity of records pre-processing, and there's a want to automate the manner for early identity of stroke symptoms so that it may be prevented. Nov 18, 2024 · Early prediction of brain stroke has been done using eight individual classifiers along with 56 other models which are designed by merging the pairs of individual models using soft and hard voting Aug 1, 2024 · Medical experts can easily reliable on such prediction models developed in our research, to obtain much better results in prediction of heart stroke severity in their early stages. The dataset consists of 303 rows and 14 columns. The cardiac stroke dataset is used in this work Nov 21, 2023 · Didn’t eliminate the records due to dataset being highly skewed on the target attribute – stroke and a good portion of the missing BMI values had accounted for positive stroke; The dataset was skewed because there were only few records which had a positive value for stroke-target attribute heart_stroke_prediction_python using Healthcare data to predict stroke Read dataset then pre-processed it along with handing missing values and outlier. AI holds significant potential in heart stroke prediction and diagnosis; however, it must confront parallel challenges to ensure precision and interpretability in its application by healthcare professionals. , ischemic or hemorrhagic stroke [1]. Jan 9, 2025 · The signs and symptoms of heart disease in patients who have recently been diagnosed or who are at risk of getting the condition are described in this dataset. 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. We use prin- is the stroke attribute is stored in the y variable. Fig. A. The experimental data were divided into training and testing datasets for further analysis and comparison. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. It employs NumPy and Pandas for data manipulation and sklearn for dataset splitting to build a Logistic Regression model for predicting heart disease. An overlook that monitors stroke prediction. heart disease: 0 if the patient does not have any heart diseases, 1 if the patient has a heart disease ever married: “No” or “Yes” work type: “Children”, “Govt job”, “Never worked”, “Private” or “Self-employed” Explore and run machine learning code with Kaggle Notebooks | Using data from Stroke Prediction Dataset Hypertension, Heart Disease and Stroke Prediction | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. heart_disease, ever_married, stroke; Categorical Oct 28, 2024 · 2. 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 Oct 21, 2024 · Reading CSV files, which have our data. info() ## Showing information about datase data. We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. read_csv('healthcare-dataset-stroke-data. Jul 3, 2021 · Dataset for stroke prediction C. Several approaches were In this project, I use the Heart Stroke Prediction dataset from WHO to predict the heart stroke. Many such stroke prediction models have emerged over the recent years. This disease is rapidly increasing in developing countries such as China, with the highest stroke burdens [6], and the United States is undergoing chronic disability because of stroke; the total number of people who died of strokes is ten times greater in Nov 2, 2023 · Among these two, the heart stroke has been considered as the most dangerous disease because heart stroke is directly connected to the brain . II. 49% and can be used for early Heart Stroke Prediction Dataset This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. Nov 26, 2021 · 2. 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. Dataset. Dec 30, 2024 · Heart-Stroke-Prediction. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Several machine learning algorithms have also been proposed to use these risk factors for predicting stroke occurrence [9], [10]. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate the prediction process for the early detection of symptoms related to stroke so that it can be prevented at an early stage. By analyzing medical records and identifying key indicators, our model can help healthcare professionals identify patients who are at high risk and take proactive measures to prevent The "Stroke Prediction Dataset" includes health and lifestyle data from patients with a history of stroke. This study evaluates three different classification models for heart stroke prediction. The dataset contains eleven clinical traits that can be used This project analyzes the Heart Disease dataset from the UCI Machine Learning Repository using Python and Jupyter Notebook. 1. This objective can be achieved using the machine learning techniques. describe() ## Showing data's statistical features Nov 1, 2022 · On the contrary, Hemorrhagic stroke occurs when a weakened blood vessel bursts or leaks blood, 15% of strokes account for hemorrhagic [5]. There is a dataset called Kaggle’s Stroke Prediction Dataset . Stages of the proposed intelligent stroke prediction framework. 67% accurate. ˛e proposed model achieves an accuracy of 95. Presence of these values can degrade the accuracy Stroke is a disease that affects the arteries leading to and within the brain. Feb 1, 2025 · The prediction models were handled a binary classification problem where the given dataset was divided into two classes (High-risk of heart stroke and Low-risk). SMOTE for Imbalanced Datasets: Enhances the model’s ability to identify the minority class, which is often the class of interest in medical datasets like stroke prediction. ITERATURE SURVEY In [4], stroke prediction was made on Cardiovascular Health Study (CHS) dataset using five machine learning techniques. The models are a Random Forest, a K-Nearest Neighbor and a Logistic Regression model. 17% for the prediction of heart stroke. With this thought, various machine learning models are built to predict the possibility of stroke in the brain. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. Perfect for machine learning and research. where P k, c is the prediction or probability of k-th model in class c, where c = {S t r o k e, N o n − S t r o k e}. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. The accuracy of the existing stroke predictions, which used a downsampling technique to balance the data, was 75%. Expand 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 paper makes use of heart stroke dataset. Fig 2 shows the dataset. 2. Domain Conception In this stage, the stroke prediction problem is studied, i. The source code for how the model was trained and constructed can be found here. The model built using sklearn's KNN module and uses the default settings. Discussion. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Accurate prediction of stroke is highly valuable for early intervention and Heart Stroke is one of the severe health hazards; therefore, early heart stroke prediction helps the society to save human lives. Framingham Heart Study Dataset Download. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. csv') data. stroke_prediction_dataset_and_WorkBook In this folder the raw dataset and workbook in excel is given. We identify the most important factors for stroke prediction. One of the greatest strengths of ML is its Sep 15, 2022 · Authors Visualization 3. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% Oct 29, 2017 · This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. sum() OUTPUT: id 0 gender 0 age 0 hypertension 0 heart_disease 0 ever_married 0 work_type 0 Residence Mar 13, 2024 · The studies dealt with the 1st dataset called (Heart Attack Analysis and Prediction Dataset) which shows that Yuan (Citation 2021) developed a framework for extracting features using the principle component analysis (PCA) and then compute a mathematical model to choose relevant attributes under suitable restrictions. Dataset for stroke prediction C. Analyze the Stroke Prediction Dataset to predict stroke risk based on factors like age, gender, heart disease, and smoking status. Presence of these values can degrade the accuracy of the model. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. Framingham Heart Disease Prediction Dataset. eookegn uhpjn fyzbbvct uzzqgxzt yuoy aullspa syv vdfuz uvn zng xlhgq lkm mhos cigfxx kqhj