Free stroke prediction dataset github This dataset is used to predict whether a patient is likely to get stroke Stroke Prediction Using Machine Learning. Updated Mar 30, 2022; Python; PREDICTION-STROKE/ ├── data/ │ ├── models/ │ │ ├── best_stroke_model. csv. The primary goal of this project is to develop a model that predicts the likelihood of a stroke based on input parameters like gender, age, symptoms, and lifestyle factors. 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. ipynb data preprocessing (takeing care of missing data, outliers, etc. Each rows provides relavant information, including gender, age, smoking status and others, about the patients. 1 gender 5110 non-null 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. Contribute to renjinirv/Stroke-prediction-dataset development by creating an account on GitHub. This project describes step-by-step procedure for building a machine learning (ML) model for stroke prediction and for analysing which features are most useful for the prediction. Stroke ML datasets from 30k to 150k Synthea patients, available in Harvard Dataverse: Synthetic Patient Data ML Dataverse. The dataset used to predict stroke is a dataset from Kaggle. It includes data preprocessing (label encoding, KNN imputation, SMOTE for balancing), and trains models like Naive Bayes, Decision Tree, SVM, and Logistic Regression. Topics Trending Collections Enterprise Enterprise platform. This project centers around the application of machine learning to train a model for categorization of individuals into two groups: those who are likely to have a stroke and those who are not. The category "Other" was excluded due to the presence of only one observation. You signed out in another tab or window. #Create two table: stroke people, normal people #At 99% CI, the stroke people bmi is higher than normal people bmi at 0. Data Dictionary Practice with imbalanced datasets. - Aroubb/Stroke-Prediction-using-Machine-Learning More than 150 million people use GitHub to discover, fork, and contribute to A web application developed with Django for real-time stroke prediction using machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks tensor kaggle-dataset stroke-prediction. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). 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. A subset of the This project predicts stroke disease using three ML algorithms - fmspecial/Stroke_Prediction We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. ; chol: Serum cholesterol (mg/dl). Symptom probabilities and weights are derived directly from textbooks like Harrison’s Principles and WHO reports, ensuring clinical relevance. The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. 7) Saved searches Use saved searches to filter your results more quickly In this application, we are using a Random Forest algorithm (other algorithms were tested as well) from scikit-learn library to help predict stroke based on 10 input features. This project utilizes the Stroke Prediction Dataset from Kaggle, available here. More than 150 million people use GitHub to discover, Prediction of brain stroke based on imbalanced dataset in two machine learning algorithms, To associate your repository with the brain-stroke-prediction topic, visit You signed in with another tab or window. This dataset was imported, cleaned, and visualized. The analysis includes linear and logistic regression models, univariate descriptive analysis, ANOVA, and chi-square tests, among others. Each row in the data provides relevant information about the patient. id: unique identifier. A stroke prediction app using Streamlit is a user-friendly tool designed to assess an individual's risk of experiencing a stroke. By inputting relevant health data such as age, blood pressure, cholesterol levels, and lifestyle factors, the app utilizes predictive algorithms to calculate the user's likelihood of having a stroke. hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension - KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. As issues are created, they’ll appear here in a This project uses six machine learning models (XGBoost, Random Forest Classifier, Support Vector Machine, Logistic Regression, Single Decision Tree Classifier, and TabNet)to make stroke predictions. ; sex: Gender (1 = Male, 0 = Female). According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. These GitHub is where people build software. It is the second leading cause of death and the third leading cause of disability globally. Each row in the data provides relavant information about the patient. There are only 209 observation with stroke = 1 and 4700 observations with stroke = 0. BMI Analysis: The mean and standard deviation of BMI were calculated for both males and females, providing insights into the health conditions of the patients. This project employs machine learning techniques to predict the likelihood of stroke occurrences based on health-related features such as:. Dependencies Python (v3. gender: "Male", "Female" or "Other" age: age of the patient. Find and fix vulnerabilities Codespaces. Each row in the data Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. Glucose Analysis: The median Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. ipynb The dataset used in this project contains the following features: 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; heart_disease: 0 if the patient doesn’t have any heart diseases, 1 if the patient has a heart disease; ever_married: "No" or "Yes" The "Cerebral Stroke Prediction" dataset is a real-world dataset used for the task of predicting the occurrence of cerebral strokes in individual. With just a few inputs—such as age, blood pressure, glucose levels, and lifestyle Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. Each row in the data provides relevant information about the The purpose of this project is to derive insight on characteristics and statistics regarding the dataset to see which factors influence whether or not a patient has had a stroke. Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction dataset. Issues are used to track todos, bugs, feature requests, and more. com/fedesoriano/stroke-prediction-dataset. 0 id 5110 non-null int64 . As issues are created, they’ll appear here in a Selected features using SelectKBest and F_Classif. - ankitlehra/Stroke-Prediction-Dataset---Exploratory-Data-Analysis Stroke Disease Prediction classifies a person with Stroke Disease and a healthy person based on the input dataset. Contribute to adnanhakim/stroke-prediction development by creating an account on GitHub. This dataset is used to predict whether a patient is likely to get stroke based on the Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. GitHub community articles Repositories. Link to Download Contribute to tjbingamon/Stroke-Prediction-Dataset development by creating an account on GitHub. - bishopce16/stroke_prediction_analysis This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Achieved high recall for stroke cases. . - rtriders/Stroke-Prediction Stroke Prediction Dataset. - arianarmw/ML01-Stroke-Prediction Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. Additionally, the project aims to analyze the dataset to identify the most significant A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. Optimized dataset, applied feature engineering, and Find and fix vulnerabilities Codespaces. For this purpose, I used the "healthcare-dataset-stroke-data" from Kaggle. It is used to predict whether a patient is likely to get stroke based on the input parameters like age, various diseases, bmi, average On the stroke, our target, column, 1 stands for getting stroke and 0 for not getting stroke. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. Focuses on data preprocessing, model evaluation, and insights interpretation to identify patterns in patient data and build predictive models. In this project, the National Health and Nutrition Examination Survey (NHANES) data from the National Center for Health CTrouton/Stroke-Prediction-Dataset This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Each row represents a patient, and the columns represent various medical attributes. Using SQL and Power BI, it aims to identify trends and corr Prediction for classification problem with imbalanced dataset about strokes with Logistic Regression, but without using ready libraries for model - GitHub - viemurr/stroke_prediction: Prediction f Decision Tree implementation based on Stroke Prediction Dataset on kaggle. using visualization libraries, ploted various To install jupyter notebook and launch other application and files at first we have to download Anaconda which is free. Instant dev environments One dataset after value conversion. Age; Gender; Hypertension; Heart Disease; Smoking Status; Average Glucose Levels; By leveraging the Random Forest algorithm, the goal is to develop a highly accurate and interpretable model to assist healthcare professionals in The project aims at displaying the charts/plots of the number of people affected by stroke based on the input parameters like smoking status, high blood pressure level, Cholesterol level, obesity level in some of the countries. This dataset has been used to predict stroke with 566 different model algorithms. Timely prediction and prevention are key to reducing its burden. Our primary objective is to develop a robust According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. We will use Flask as it is a very light web framework to handle Gender Distribution: A basic frequency table was generated to explore gender distribution in the dataset. ; cp: Chest pain type (0-3). - NVM2209/Cerebral-Stroke-Prediction. Evaluated models, addressed overfitting, and documented the process in a Jupyter Notebook. The dataset used in the development of the method was the open-access Stroke Prediction dataset. Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. 47 - 2. main 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. 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. Each row in the data GitHub Gist: instantly share code, notes, and snippets. ; Balanced Rare Events gender age hypertension heart_disease ever_married work_type Residence_type avg_glucose_level bmi smoking_status stroke Female 61 0 0 Yes Self-employed Rural 202. 4) Which type of ML model is it and what has been the approach to build it? This is a classification type of ML model. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Classification into 0 (no stroke) or 1 (stroke) Steps: Loading the dataset and required packages; The dataset was synthetically generated based on statistical distributions obtained from real-world medical studies. One can roughly classify strokes into two main types: Ischemic stroke, which is due to lack of blood flow, and hemorrhagic stroke, due to 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. csv │ │ └── stroke_data_final. ) available in preparation. This This project predicts stroke occurrences using machine learning on a healthcare dataset. 2% of total deaths were due to stroke. list of steps in this path are as below: exploratory data analysis available in P2. Data Preprocessing: AWS Data Wrangler is used to preprocess the data. This project aims to use Decision Tree to predict if somone is at great risk of stroke based on existing conditions. #Hypothesis: people who had stroke is higher in bmi than people who had no stroke. Stroke is a type of cardiovascular disease, with two types: ischemic and hemorrhagic stroke. Show Gist 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. 21 N/A never smoked 1 Male 80 0 1 Yes Private Rural 105. Stroke prediction with machine learning and SHAP algorithm using Kaggle dataset - Silvano315/Stroke_Prediction This major project, undertaken as part of the Pattern Recognition and Machine Learning (PRML) course, focuses on predicting brain strokes using advanced machine learning techniques. The best-performing model is deployed in a web-based application, with future developments including real-time data integration. main. A balanced sample dataset is created by combining all 209 observations with stroke = 1 and 10% of the observations with stroke = 0 which were obtained by random sampling from the 4700 observations. The dataset consists of 303 rows and 14 columns. The steps involve imputing null values, encoding categorical variables, scaling numerical variables, and applying Synthetic Minority Over-sampling Technique (SMOTE) to tackle the issue of unbalanced class You signed in with another tab or window. joblib │ ├── processed/ │ │ ├── processed_stroke_data. More than 100 million "The Use of Deep Learning to Predict Stroke Patient Mortality" by machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks tensor kaggle-dataset stroke-prediction Updated Mar 30, 2022; Python; alexvolchek615 11 clinical features for predicting stroke events. Learn more Stroke Prediction dataset from Kaggle URL: https://www. joblib │ │ ├── model_metadata. age: Age of the patient. Contribute to nithinp300/Stroke-Prediction-Dataset development by creating an account on GitHub. 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 You signed in with another tab or window. Reload to refresh your session. - naldye/Decision-Tree Implemented Decision Trees, SVM, and KNN to predict stroke risk using a Kaggle dataset. The purpose of this is to help create a model that can determine if a patient is likely to get a stroke based on the metabolic parameters provided. 5 never smoked 1 Hi all,. DataSciencePortfolio 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. Contribute to haoyu-jia/Stroke-Prediction development by creating an account on GitHub. Input data is preprocessed and is 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 detection project developed using R. Medical Literature Integration: . This dataset is used to predict whether a patient is likely to get a stroke based on the input parameters like gender, age, various diseases, and smoking status. Later tuned model by selecting variables with high coefficient > 0. Contribute to ChastityB/Stroke_Predictions_Dataset development by creating an account on GitHub. 92 32. Standard codes for the stroke data: synthea-stroke-dataset-codes. # Column Non-Null Count Dtype . Contribute to Rasha-A21/Stroke-Prediction-Dataset development by creating an account on GitHub. - mriamft/Stroke-Prediction Data: The data used for this project is a healthcare stroke dataset stored in an AWS S3 bucket. ; fbs: Fasting blood sugar > 120 mg/dl (1 = True; 0 = False). csv │ └── raw/ │ └── healthcare-dataset An exploratory data analysis (EDA) and various statistical tests performed on a dataset focused on stroke prediction. This package can be imported into any application for adding security features. To ensure accuracy, probability-weighted sampling was used, incorporating risk factor dependencies like age, high Contribute to ChastityB/Stroke_Predictions_Dataset development by creating an account on GitHub. GitHub Gist: BhanuMotupalli / Heart Stroke Prediction Dataset. Machine Learning project for stroke prediction analysis using clustering and classification techniques. kaggle. Version 1 assumed linear risk increase with age, but Version 2 uses a sigmoid function to model the exponential risk rise after 50. 3 GitHub is where people build software. The goal is to develop a robust model for stroke prediction. Initially Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. I used Logistic Regression with manual class weights since the dataset is imbalanced. ; trestbps: Resting blood pressure (mm Hg). csv │ │ ├── stroke_data_engineered. 82 bmi #Conclusion: Reject the this project contains a full knowledge discovery path on stroke prediction dataset. Created March 22, 2023 21:03. Balance dataset¶ Stroke prediction dataset is highly imbalanced. The dataset ensures a 50:50 distribution between individuals at risk and not at risk, making it balanced for both classification and regression tasks. In 2016, 10. To determine which model is the best to make stroke predictions, I plotted the area under the Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. Project Overview: Dataset predicts stroke likelihood based on patient parameters (gender, age, diseases, smoking). joblib │ │ └── optimized_stroke_model. You switched accounts on another tab or window. AI-powered developer Foreseeing the underlying risk factors of stroke is highly valuable to stroke screening and prevention. Instant dev environments GitHub community articles Repositories. ; Non-Linear Aging: . yus drtkryw whq psiyvwv xctkto wlkwcir umxk hbmync kadwp lhj jamms ltio ahor ymdk ofzok