Heart stroke prediction dataset Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. The ML algorithm that Feb 7, 2024 · Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. Jun 25, 2020 · Authors of [12] tested various models on the dataset provided by Kaggle for stroke prediction. Stroke Prediction Dataset 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 Jul 1, 2021 · Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Feb 5, 2024 · Heart attack is a catch-all term for a variety of conditions affecting the heart. There is a dataset called Kaggle’s Stroke Prediction Dataset . Most of our healthy bmi sample between 25 and 75 years old is populated by females. L. 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. 892 in one cohort analysis. We have found an increasing trend in our analysis which will contribute to advancing the knowledge in the field of heart stroke prediction. 5110 observations with 12 characteristics make up the data. 15 2019. csv") str stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. We use prin- Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. In another research [18], the authors worked on the extraction of relevant risk factors form a large feature space for an efficient heart disease prediction. 5649 Total Sum of Squares : 29. We systematically We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. it is to predict heart attacks, the prediction process must be automated in order to minimize risks and notify patients well in advance. The models are a Random Forest, a K-Nearest Neighbor and a Logistic Regression model. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Similar to this, CT pictures are a common dataset in stroke. head(10) ## Displaying top 10 rows data. 文章浏览阅读2k次，点赞4次，收藏8次。本文介绍了使用Kaggle上的stroke预测数据集进行机器学习实战的过程，涉及数据加载、EDA、特征工程、数据预处理、模型选择和评估。 Stroke Prediction K-Nearest Neighbors Model. - ajspurr/stroke_prediction Mar 4, 2022 · Heart disease and strokes have rapidly increased globally even at juvenile ages. Why Choose This Dataset? 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 "Framingham" heart disease dataset has 15 attributes and over 4,000 records. 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. The data pre-processing techniques inoculated in the proposed model are replacement of the missing 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 Sep 15, 2022 · Authors Visualization 3. assessed the efficacy of machine learning techniques in predicting strokes by employing the Kaggle stroke prediction dataset. To develop the first module, which involves predicting heart disease, machine learning models were trained and tested using structured patient information such as age, gender, and hypertension history, as well as real-time clinical data like heart rate and blood pressure. 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. e. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', 'Work Type Nov 26, 2021 · Dataset. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Data imputation, feature selection, data preprocessing is May 26, 2023 · In this paper, three modules were designed and developed for heart disease and brain stroke prediction. [2]. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Presence of these values can degrade the accuracy This project analyzes the Heart Disease dataset from the UCI Machine Learning Repository using Python and Jupyter Notebook. Early and precise prediction is crucial to providing effective preventive healthcare interventions. Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. 2 Pre Processing of Dataset. The target of the dataset is to predict the 10-year risk of coronary heart disease (CHD). The experimental data were divided into training and testing datasets for further analysis and comparison. 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. The datasets used are classified in terms of 12 parameters like hypertension, heart disease, BMI, smoking status, etc. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. Jun 14, 2024 · The analysis of the stroke prediction dataset revealed several significant findings regarding the predictive factors associated with stroke incidence. 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 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. 3. Oct 1, 2024 · In 10 studies, the accuracy of the stroke prediction algorithm was above 90%. This Mar 22, 2023 · Heart Stroke Prediction Dataset This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. In this paper, the heart stroke dataset is used. e value of the output column stroke is either 1 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 The current American Heart Association/American Stroke Association prevention of stroke guidelines recommend use of risk prediction models to optimize screening and interventions. 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. data=pd. We are predicting the stroke probability using clinical measurements for a number of patients. In ten investigations for stroke issues, Support Vector Machine (SVM) was found to be the best models. 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. 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. 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. Apr 12, 2023 · Early efforts to develop ML algorithms for predicting stroke risk in AF patients have shown some promise, and have achieved an AUC as high as 0. 49% and can be used for early Sep 29, 2020 · Machine learning (ML) is a branch of artificial intelligence (AI) that is increasingly utilized within the field of cardiovascular medicine. data = read. In this research article, machine learning models are applied on well known heart stroke classification data-set. 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. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. openresty Aug 14, 2024 · Rates and Trends in Heart Disease and Stroke Mortality Among US Adults (35+) by County, Age Group, Race/Ethnicity, and Sex – 2000-2019 recent views U. With help of this CSV, we will try to understand the pattern and create our prediction model. 71), only retinal characteristics (AUROC, 0. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. of Points : 102 Between-group Sum of Squares : 20. In CHS dataset, missing value and large number of other attribute beside the stroke makes it very challenging for direct use. 556 136. Fig. heart_stroke_prediction_python using Healthcare data to predict stroke Read dataset then pre-processed it along with handing missing values and outlier. Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. 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. In our research, we harnessed the potential of the Stroke Prediction Dataset, a valuable resource containing 11 distinct attributes. There were 5110 rows and 12 columns in this dataset. Purpose of dataset: To predict stroke based on other attributes. A. 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). In the blood pressure, diabetes and heart disease as major risk factors responsible for stroke attack in an individual. This is a demonstration for a machine learning model that will give a probability of having a stroke. However, a systematic analysis of the risk factors is missing. In predictive analytics, many studies were proposed to get alerts Jul 7, 2023 · Our ML model uses a dataset for survival prediction to determine a patient's likelihood of suffering a stroke based on inputs including gender, age, various illnesses, and smoking status. ˛e proposed model achieves an accuracy of 95. ere were 5110 rows and 12 columns in this dataset. Our study considers Mar 29, 2020 · 3. 55% using the RF classifier for the stroke prediction dataset. . 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 Jan 24, 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. Nov 1, 2022 · Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. Many such stroke prediction models have emerged over the recent years. Specifically, this report presents county (or county equivalent) estimates of heart Aug 22, 2021 · Every 40 seconds in the US, someone experiences a stroke, and every four minutes, someone dies from it according to the CDC. This objective can be achieved using the machine learning techniques. Mridha et al. As an optimal solution, the authors used a combination of the Decision Tree with the C4. Stages of the proposed intelligent stroke prediction framework. Link: healthcare-dataset-stroke-data. Interestingly, the findings align with another previously The "Stroke Prediction Dataset" includes health and lifestyle data from patients with a history of stroke. With my interest in healthcare and parents aging into a new decade, I chose this Stroke Prediction Dataset from Kaggle for my Python project. About. 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. The suggested work uses various data mining techniques, including SVM, Neural Network and Mar 10, 2023 · In order to predict the heart stroke, an effective heart stroke prediction system (EHSPS) is developed using machine learning algorithms. 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]. 11 clinical features for predicting stroke events. Accurate prediction of stroke is highly valuable for early intervention and Stroke is a leading cause of death and disability worldwide, with about three-quarters of all stroke cases occurring in low- and middle-income countries (LMICs). 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. The dataset consists of 303 rows and 14 columns. Our dataset, in contrast to most others, concentrates on characteristics that would be significant risk factors for a brain stroke. Apr 20, 2023 · Stroke Prediction Dataset have been used to conduct the proposed experiment. Title: Stroke Prediction Dataset. This project uses Kaggle's Stroke Prediction dataset to predict heart stroke where the classes are not balanced. S. The results of this research could be further affirmed by using larger real datasets for heart stroke prediction. , ischemic or hemorrhagic stroke [1]. Project Thesis This project employs machine learning principles on extensive existing datasets to predict stroke risk based on Oct 21, 2024 · Reading CSV files, which have our data. Department of Health & Human Services — This dataset documents rates and trends in heart disease and stroke mortality. Mar 15, 2024 · The utilization of image data for stroke prediction is not consistently accessible, involves high costs, and can be time-consuming, posing challenges for swift diagnosis. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. Jul 2, 2024 · Stroke poses a significant health threat, affecting millions annually. In the whole dataset about 60% of baseline attributes are missing and having some features which are not directly related to the stroke (i. Information about the model and application. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset By detecting high-risk individuals early, appropriate preventive measures can be taken to reduce the incidence and impact of stroke. Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. A recent figure of stroke-related cost almost reached $46 billion. 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. 2. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. sum() OUTPUT: id 0 gender 0 age 0 hypertension 0 heart_disease 0 ever_married 0 work_type 0 Residence stroke prediction. describe() ## Showing data's statistical features has been carried out on the prediction of heart stroke but very few works show the risk of a brain stroke. Research Drive. May 23, 2024 · In fact, (1) the average age of stroke patients is much higher than the average age of those who do not suffer from stroke disease, and due to the decreased immunity of the elderly, the risk of suffering from various diseases will be higher; (2) the average blood glucose of stroke patients is higher, and the results of related studies have Dec 14, 2023 · Dataset. is the stroke attribute is stored in the y variable. Also, the 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. In the following subsections, we explain each stage in detail. Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. 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. 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 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]. 2 Performed Univariate and Bivariate Analysis to draw key insights. The dataset contains eleven clinical traits that can be used Sep 22, 2023 · About Data Analysis Report. openresty Nov 1, 2019 · Most of the existing researches about stroke prediction are concerned with the complete and class balance dataset, but few medical datasets can strictly meet such requirements. In the proposed model, heart stroke prediction is performed on a dataset collected from Kaggle. This study evaluates three different classification models for heart stroke prediction. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. 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 . May 8, 2024 · accuracy score of 92. The model built using sklearn's KNN module and uses the default settings. Our study focuses on predicting Nov 8, 2023 · About Data Analysis Report. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. 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. This includes prediction algorithms which use "Healthcare stroke dataset" to predict the occurence of ischaemic heart disease. The accuracy of the existing stroke predictions, which used a downsampling technique to balance the data, was 75%. 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. 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 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. Framingham Heart Disease Prediction Dataset. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). In recent years, some DL algorithms have approached human levels of performance in object recognition . Domain Conception In this stage, the stroke prediction problem is studied, i. Objective 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. 8, 21, 22, 25, 27-32 Among these 10 studies, five recommended the RF algorithm as the most efficient algorithm in stroke prediction. The cardiac stroke dataset is used in this work Stroke is a disease that affects the arteries leading to and within the brain. Nov 1, 2022 · The proposed technique selected 9 important input features out of 28 based on the knowledge provided for heart stroke prediction. teenagers. 65), and both (AUROC, 0. To review, open the file in an editor that reveals hidden Unicode characters. Each row in the data provides relavant information about the patient. Bashir et al. Oct 28, 2024 · 2. info() ## Showing information about datase data. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. The dataset is obtained from Kaggle and is available for download. Each row represents a patient, and the columns represent various medical attributes. 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]. This research investigates the application of robust machine learning (ML) algorithms, including Jan 1, 2020 · Summary of Diagnostics No. However, these studies pay less attention to the predictors (both demographic and behavioural). A dataset containing all the required fields to build robust AI/ML models to detect Stroke. frame. As a limitation, there could be more advanced initial centroid selection methods in future which will be directly incorporated in K-means Clustering algorithm. 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 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. Synthetically generated dataset containing Stroke Prediction metrics. This RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. Importing the necessary libraries Stroke Prediction and Analysis with Machine Learning - nurahmadi/Stroke-prediction-with-ML. Recall is very useful when you have to Dec 28, 2024 · This retrospective observational study aimed to analyze stroke prediction in patients. 2 Sep 21, 2021 · <class 'pandas. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. Learn more Aug 22, 2023 · 303 See Other. 2. For the incomplete data, a missing value imputation method based on iterative mechanism has shown an acceptable prediction accuracy [14] , [15] . 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 The naive Bayes, compared to the other algorithms, achieved a better accuracy, with 82% for the prediction of stroke. Dec 8, 2020 · The dataset consisted of 10 metrics for a total of 43,400 patients. The value of the output column stroke is either 1 or 0. Aug 2, 2023 · Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. Framingham Heart Study Dataset Download. Discussion. One of the greatest strengths of ML is its Summary. Jun 1, 2024 · 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. 3. Jan 1, 2022 · The pattern of the attributes as per the provided dataset was monitored for accurate prediction of heart stroke in the patients. of Clusters : 2 No. These Sep 28, 2022 · The dataset contains 13 features, which report clinical, body, and lifestyle information responsible for heart failure. prediction of stroke. 34 Whereas CHADS 2 and CHA 2 DS 2-VASc use 6–7 features to stratify stroke risk, an attention-based DNN model identified up to 48 features that influenced stroke risk using stroke_prediction_dataset_and_WorkBook In this folder the raw dataset and workbook in excel is given. csv("stroke_data. core. Apr 1, 2022 · Attempts have been made to identify predictors of recurrent stroke using Cox regression without developing a prediction model. The experiments used five different classifiers, NB, SVM, RF, Adaboost, and XGBoost, and three feature selection methods for brain stroke prediction, MI, PC, and FI. Objectives:-Objective 1: To identify which factors have the most influence on stroke prediction Predicting probability of heart disease in patients. 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 27, 2020 · The brain is an energy-consuming organ that heavily relies on the heart for energy supply. read_csv('healthcare-dataset-stroke-data. 13,14 Logistic regression was used with only clinical and imaging variables (AUROC, 0. The source code for how the model was trained and constructed can be found here. Presence of these values can degrade the accuracy of the model. heart stroke prediction is performed the use of a dataset Feb 1, 2025 · One limitation of this research was the size of the dataset used. Learn more Oct 7, 2024 · 303 See Other. In this project, I use the Heart Stroke Prediction dataset from WHO to predict the heart stroke. One of the greatest strengths of ML is its 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 can be downloaded from the Kaggle stroke dataset. of Clusters Items Ages (in Sum) Sum of maximum heart rate Disease Cluster1 75 49. 1 China has the largest stroke burden in the world, and accounts for approximately one-third of global stroke mortality with 34 million prevalent cases and 2 million deaths in 2017. The output attribute is a Most of the high glucose sample is populated by either children or people over 50 years old. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. 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. With this thought, various machine learning models are built to predict the possibility of stroke in the brain. heart_disease, ever_married, stroke; Categorical Dec 30, 2024 · Heart-Stroke-Prediction. , 2023: 25 papers Explore and run machine learning code with Kaggle Notebooks | Using data from Stroke Prediction Dataset Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 1. entry in some attribute are blank due to forbidden to answer or unknown). [ ] Stroke is the 2nd leading cause of death globally, and is a disease that affects millions of people every year: Wikipedia - Stroke . Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. Several studies have been conducted using the Stroke Prediction Dataset in recent years, and the results have been Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various 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. An overlook that monitors stroke prediction. Structure. isnull(). 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 Dec 5, 2021 · Many such stroke prediction models have emerged over the recent years. 5 algorithm, Principal Component Analysis, Artificial Neural Networks, and Support Vector Apr 17, 2021 · 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. ITERATURE SURVEY In [4], stroke prediction was made on Cardiovascular Health Study (CHS) dataset using five machine learning techniques. II. csv. It is essentially how computers make sense of data and About. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. The process reduces the intake of blood and internally causes a pseudo vacuum of air bubbles leading to a stroke which can be identified with high-end Jun 24, 2022 · 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. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. Tan et al. Hybrid models using superior machine learning classifiers should also be implemented and tested for stroke prediction. Firstly, it was noted that the target variable, May 1, 2024 · This study proposed a hybrid system for brain stroke prediction (HSBSP) using data from the Stroke Prediction Dataset. 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. No records were removed because the dataset had a small subset of missing values and records logged as unknown. 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 . 85 Table 2: Chest Pain Type: Asymptomatic No. 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. 853 124. This experiment was also conducted to compare the machine learning model performance between Decision Tree, Random Jul 1, 2023 · The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. 59 Negative 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. This dataset documents rates and trends in heart disease and stroke mortality. Dataset for stroke prediction C. 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. 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. K. 03 Positive Cluster2 27 48. This paper makes use of heart stroke dataset. Fig 2. e stroke prediction dataset [16] was used to perform the study. Data Pre-processing The dataset obtained contains 201 null values in the BMI attribute which needs to be removed. - ebbeberge/stroke-prediction Heart Stroke is one of the severe health hazards; therefore, early heart stroke prediction helps the society to save human lives. 17% for the prediction of heart stroke. DataFrame'> Int64Index: 4088 entries, 25283 to 31836 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 gender 4088 non-null object 1 age 4088 non-null float64 2 hypertension 4088 non-null int64 3 heart_disease 4088 non-null int64 4 ever_married 4088 non-null object 5 work_type 4088 non-null object 6 Residence_type 4088 non-null Nov 26, 2021 · 2. View Notebook Download Dataset The system proposed in this paper specifies. 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. The stroke prediction dataset was used to perform the study. The dataset can be found in the repository or can be downloaded from Kaggle. Jul 3, 2021 · Dataset for stroke prediction C. 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. Dataset. csv') data. Fig 2 shows the dataset. The categories of support vector machine and 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 Personalized Medicine: The dataset can help develop tools for personalized stroke risk assessments based on individual patient profiles. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. 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. In addition, the majority of studies are in stroke diagnosis whereas the majority of studies are in stroke treatment, indicating a research gap that needs to be filled. The following table provides an extract of the dataset used in this article. machine-learning deep-learning cnn neural-networks breast-cancer-prediction classification-model diabetes-prediction heart-disease-prediction malaria-prediction liver-disease-prediction kidney-disease-prediction multiple-disease-prediction pneumonia-prediction 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 Nov 6, 2020 · This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. Deep learning is capable of constructing a nonlinear Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Year: 2023. In this research work, with the aid of machine learning (ML 2. , 2023: 12 papers: 2019–2022: The paper reviews 12 studies on machine learning for stroke prediction, focusing on techniques, datasets, models, performance, and limitations. To enhance the accuracy of the stroke prediction model, the dataset will be analyzed and processed using various data science methodologies and algorithms. In addition, effect of pre-processing the data has also been summarized. Several machine learning algorithms have also been proposed to use these risk factors for predicting stroke occurrence [9], [10]. Check for Missing values # lets check for null values df. 74) whereby performance was measured on the same data used for model development (no separate test data). Oct 29, 2017 · This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. The features were selected based on their individual ranks. 285 Within-group Sum of Squares : 9. The dataset included 401 cases of healthy individuals and 262 cases of stroke patients admitted in hospital Dec 26, 2021 · In this experiment, we implement a process of stroke risk prediction from our dataset using the various machine learning algorithms. Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate Nov 9, 2024 · Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. It employs NumPy and Pandas for data manipulation and sklearn for dataset splitting to build a Logistic Regression model for predicting heart disease. Jan 28, 2025 · In this article, we will be closely working with the heart disease prediction using Machine Learning and for that, we will be looking into the heart disease dataset from that dataset we will derive various insights that help us know the weightage of each feature and how they are interrelated to each other but this time our sole aim is to detect the probability of person that will be affected The review aimed to analyze the different studies using the Healthcare Kaggle stroke dataset with various performance metrics. Feb 1, 2022 · The augmented dataset includes age, BMI, average glucose level, heart disease, hypertension, ever-married, and stroke label features. Therefore, the stroke must be precisely predicted to begin treatment as soon as possible. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke patient survival based on a comprehensive set of factors. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. iosg hij srnk xuseqa duivvh hswpm ukcamkf qoomrc yozx rli umaxto ywtl kooquya hstvq evzst