Stroke prediction dataset github The dataset contains various features like gender, age, hypertension status, heart disease status, marital status, work type, residence type, average glucose level, BMI, and smoking status. - coderjones/stroke_prediction. 42 Explanatory Data Analysis -Patients between the age of 50-80 years old are at greater risk of getting a stroke. The model used for predictions is trained on a dataset of healthcare records. Contribute to Abdalla-Elshamy2003/Stroke_Prediction_Dataset development by creating an account on GitHub. GitHub Copilot. 2% classification accuracy via 5-fold cross validation approach. However, it does not suggest that a 25 yr old single male who smokes two packs of cigarette a day will not get stroke. In this case, I used SMOTE to oversample the minority class (stroke) to get a more balanced dataset. Find and fix vulnerabilities If not available on GitHub, the notebook can be accessed on nbviewer, or alternatively on Kaggle. Reload to refresh your session. 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 Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. Prediction of brain stroke based on imbalanced dataset in 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. Objective: Create a machine learning model predicting patients at risk of stroke. This repository contains an analysis of the Healthcare Stroke Prediction Dataset. Contribute to TomerSh135/Stroke-Prediction-Dataset development by creating an account on GitHub. csv file can be used to predict whether a patient is likely to get stroke based on several attributes like gender, age, various diseases, and smoking status. Feature distributions are close to, but not exactly the same, as the original. I used Logistic Regression with manual class weights since the dataset is imbalanced. - KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset 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. We aim to identify the factors that con Introduction¶ The dataset for this competition (both train and test) was generated from a deep learning model trained on the Stroke Prediction Dataset. The dataset contains 5110 observations - navidnaji/Stroke-prediction The Dataset_Stroke. An Exploratory Data Analysis on the Stroke Prediction Dataset to understand the various parameters affecting stroke and gain some insights on the same. Input Features: id: A unique identifier for each patient in the dataset. Divide the data randomly in training and testing Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. Dec 7, 2024 · 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. This project utilized a dataset containing various patient characteristics, including demographics, health conditions, and lifestyle habits Aimed to identify individuals at higher risk of stroke for early intervention and preventative measures 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 Stroke Disease Prediction classifies a person with Stroke Disease and a healthy person based on the input dataset. See users view of app here: https://ml-stroke-predictions. ; Symptom probabilities (e. - rtriders/Stroke-Prediction Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. Write better code with AI Security. Optimized dataset, applied feature engineering, and implemented various algorithms. Contribute to tejaharshini4455/Stroke_Prediction_Dataset development by creating an account on GitHub. Mar 7, 2025 · 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. Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for Analysis of the Stroke Prediction Dataset to provide insights for the hospital. id: unique identifier. gender: "Male", "Female" or "Other" age: age of the patient. Performing EDA, data visualization, statistical inference, machine learning, model deployment. hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension This project predicts stroke disease using three ML algorithms - fmspecial/Stroke_Prediction Contribute to mnbpdx/stroke-prediction-dataset development by creating an account on GitHub. Impact: You signed in with another tab or window. Handling Class Imbalance: Since stroke cases are rare in the dataset (class imbalance), we applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples of the minority class and balance the dataset. [ ] 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. Stroke dataset Classification. , hypertension, chest pain) scale with age (see Medical Validity). Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. Contribute to nithinp300/Stroke-Prediction-Dataset development by creating an account on GitHub. Sign in Product Meanwhile, other traits we studied from the dataset that could lead to stroke include : female, married, living at urban, working in private sector and never smoked. This contains a stroke dataset from kaggle which was used for predicting the possibility of a stroke, using Linear regression, SVM, and KNN 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. stroke_prediction_dataset_and_WorkBook In this folder the raw dataset and workbook in excel is given. - ankitlehra/Stroke-Prediction-Dataset---Exploratory-Data-Analysis Sep 18, 2024 · You signed in with another tab or window. GitHub repository for stroke prediction project. 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 Healthcare Dataset. Project Overview: Dataset predicts stroke likelihood based on patient parameters (gender, age, diseases, smoking). DataSciencePortfolio Analysis of stroke prediction dataset. We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. - mriamft/Stroke-Prediction You signed in with another tab or window. - GitHub - Assasi Stroke Prediction This project goes through data exploration, cleaning and training of a neural network that uses entity embedding to map categorical variables. Selected features using SelectKBest and F_Classif. pode auxiliar profissionais a tomarem decisões mais proativas, nesse sentido, utilizamos um banco de dados de um Hackathon para tentar prever a probabilidade de acontecer um acidente vascular cerebral. 4% is achieved. - NVM2209/Cerebral-Stroke-Prediction Saved searches Use saved searches to filter your results more quickly Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. age: The age You signed in with another tab or window. Topics Trending Contribute to erickkartiadi/stroke-prediction-dataset development by creating an account on GitHub. Stroke Prediction Dataset by using Machine Learning - AsifIkbal1/-Stroke-Prediction-Dataset According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. The motivation for this notebook came from my participation in Playground Series Season 3, Episode 2 Kaggle competition, which used a synthetic version of the Stroke Prediction dataset and raised some questions about model evaluation. The analysis includes linear and logistic regression models, univariate descriptive analysis, ANOVA, and chi-square tests, among others. 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. Using SQL and Power BI, it aims to identify trends and correlations that can aid in stroke risk prediction, enhancing understanding of health outcomes in different demographics. Learn more According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. An exploratory data analysis (EDA) and various statistical tests performed on a dataset focused on stroke prediction. . Dependencies Python (v3. This project implements various neural network models to predict strokes using the Stroke Prediction Dataset from Kaggle. Later tuned model by selecting variables with high coefficient > 0. Stroke prediction dataset. 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 Data Set Information: 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. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. A. - bpalia/StrokePrediction This GitHub repository contains the code for a Stroke Prediction App. - GitHub - TomasJurkstas/stroke 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. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. - ajspurr/stroke_prediction About. Contribute to BrunoMeloSlv/Stroke-Prediction-Dataset development by creating an account on GitHub. - skp163/Stroke_Prediction. - ragh4869/Stroke-Prediction-Analysis You signed in with another tab or window. Each row in the data provides relavant information about the patient. Resources You need to download ‘Stroke Prediction Dataset’ data using the library Scikit learn; ref is given below. One can roughly classify strokes into two main types: Ischemic stroke, which is due to lack of blood flow, and hemorrhagic stroke, due to bleeding. 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. Age-Accurate Risk Modeling:. machine learning model to predict individuals chances of having a stroke. Input data is preprocessed and is given to over 7 models, where a maximum accuracy of 99. Feel free to use the original dataset as part of this competition This project utilizes the Stroke Prediction Dataset from Kaggle, available here. The app is built using Streamlit, and it predicts the likelihood of a stroke based on real-life data. GitHub community articles Repositories. Using the “Stroke Prediction Dataset” available on Kaggle, our primary goal for this project is to delve deeper into the risk factors associated with stroke. Stroke Prediction Dataset. The dataset consists of 11 clinical features which contribute to stroke occurence. 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. Navigation Menu Toggle navigation. This is Stroke Prediction Dataset. Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. Contribute to ThasmiaS/Stroke-Prediction development by creating an account on GitHub. - bishopce16/stroke_prediction_analysis The dataset contains 5110 unique records with 12 attributes for each, collecting from 2995 females and 2115 males. herokuapp. Using a Kaggle dataset to do a stroke prediction analysis. Stroke risk now follows a sigmoidal curve (sharp increase after age 50), reflecting real-world epidemiological trends. 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. 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. 47 - 2. Stroke and BMI have the strongest correlation with 0. 82 bmi #Conclusion: Reject the null hypothesis, finding that higher bmi level is likely 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. As per the WHO (World Health Organization) stroke is the 2nd leading cause of dead globally. Stroke Prediction This project aims to predict the likelihood of stroke in patients using various machine-learning techniques. g. Resources Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. com/ Hi all, This is the capstone project on stroke prediction dataset. For a small dataset of 992 samples, you could get high accuracy by predicting all cases as negative, but you won't detect any potential stroke victims. Using SQL and Power BI, it aims to identify trends and corr You signed in with another tab or window. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Project Title: "Cerebral-Stroke-Prediction" for predicting whether a patient will suffer from a stroke, in order to provide timely interventions. 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. The output attribute is a 11 clinical features for predicting stroke events. You switched accounts on another tab or window. It is used to predict whether a patient is likely to get stroke based on the input parameters like age, various diseases, bmi, average glucose level and smoking status. Contribute to Rasha-A21/Stroke-Prediction-Dataset development by creating an account on GitHub. Achieved high recall for stroke cases. About. The dataset used in the development of the method was the open-access Stroke Prediction dataset. Contribute to NabilRaiyan/Stroke-Prediction-Dataset development by creating an account on GitHub. You signed in with another tab or window. Contribute to kushal3877/Stroke-Prediction-Dataset development by creating an account on GitHub. Contribute to URJ5329/Stroke_Prediction_Dataset development by creating an account on GitHub. #Create two table: stroke people, normal people #At 99% CI, the stroke people bmi is higher than normal people bmi at 0. I have done EDA, visualisation, encoding, scaling and modelling of dataset. The goal of this project was to explore the dataset, clean and preprocess the data, and perform basic statistical analysis and predictive modeling. Timely prediction and prevention are key to reducing its burden. 3 Contribute to emilyle91/stroke-prediction-dataset-analysis development by creating an account on GitHub. This package can be imported into any application for adding security features. Find and fix vulnerabilities Clique aqui para realizar um pequeno teste! Pensamos que I. The "Cerebral Stroke Prediction" dataset is a real-world dataset used for the task of predicting the occurrence of cerebral strokes in individual. gender: The gender of the patient, which can be "Male" or "Female". A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. Analysis of the Stroke Prediction Dataset provided on Kaggle. Brain Stroke Prediction- Project on predicting brain stroke on an imbalanced dataset with various ML Algorithms and DL to find the optimal model and use for medical applications. 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. The goal is to optimize classification performance while addressing challenges like imbalanced datasets and high false-positive rates in medical predictions. Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. The last column contains ‘1’ if the patient had stroke and ‘0’ if he or she hadn’t. [5] 2. You signed out in another tab or window. Contribute to kevin-wijaya/Stroke-Sampling-Prediction development by creating an account on GitHub. Additionally, the project aims to analyze the dataset to identify the most significant features that contribute to stroke prediction. #Hypothesis: people who had stroke is higher in bmi than people who had no stroke. Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. The dataset for the project has the following columns: id: unique identifier; gender: "Male", "Female" or "Other" age: age of the patient; hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. 7) Predicting brain stroke by given features in dataset. The rather simple neural network achieves approximately 98. The dataset used in this project contains information about various health parameters of individuals, including: id: unique identifier; gender: "Male", "Female" or "Other" Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. otlgbs rfzx gsiou loou ths waen qvlze kxvwa cenbz dzqna nlyvlqnww wyck aokixpzu soudsaw cnb