Stroke prediction dataset github - baisali14/Hypertension-Heart-Disease-and-Stroke-Prediction-using-SVM This repository holds a machine learning model trained using SVM to predict whether a person has hypertension or not, the person has heart disease or not The incidence of stroke increases significantly with age, however over 60% of strokes happen to people under the age of 70 and 16% happen to those under the age of 50. - bpalia/StrokePrediction According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Achieved high recall for stroke cases. - EDA-Clustering-Classification-on-Stroke-Prediction-Dataset/README. Using SQL and Power BI, it aims to identify trends and corr You signed in with another tab or window. o Replacing the outlier values with the mode. Find and fix vulnerabilities Analysis of the Stroke Prediction Dataset to provide insights for the hospital. With stroke being a common health issue within the United States, it is important to analyze data related to this issue to determine ways to further prevent it from occurring. Mechine Learnig | 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. o Convert categorical variables to numbers by LabelEncoder in sklearn. - GitHub - erma0x/stroke-prediction-model: Data exploration, preprocessing, analysis and building a stroke model prediction in the life of the patient. I perform EDA using Pandas, seaborn, matplotlib library In this I used machine learning algorithms for categorical output like, logistic regression, Decision tree, Random forest, KNN, Adaboost, Machine Learning techniques including Random Forest, KNN , XGBoost , Catboost and Naive Bayes have been used for prediction. Using various datasets, it was trained to predict the stroke. So i used sampling technique to solve that problem. - hernanrazo/stroke-prediction-using-deep-learning The dataset specified in data. A web application developed with Django for real-time stroke prediction using logistic regression. Repository for stroke prediction system based on the Kaggle Dataset . The goal of this project is to build a model with an accuracy of 93% to predict stroke. The aim of this project is to predict the probability of having a stroke using a dataset from Kaggle. Preventative measures could be taken to save patients from the detrimental side effects of having a stroke. Project Overview: Dataset predicts stroke likelihood based on patient parameters (gender, age, diseases, smoking). Version 1 assumed linear risk increase with age, but Version 2 uses a sigmoid function to model the exponential risk rise after 50. Find and fix vulnerabilities Saved searches Use saved searches to filter your results more quickly Stroke Prediction Dataset. Our primary objective is to develop a robust predictive model for identifying potential brain stroke occurrences, a Plan and track work Code Review 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. Each row in Gender Distribution: A basic frequency table was generated to explore gender distribution in the dataset. Write better code with AI Security. GitHub Copilot. Sign in Product 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. - Medical Literature Integration: . The decision made based on the result will be summarized using powerpoint presentation. Dependencies Python (v3. Contribute to BrunoMeloSlv/Stroke-Prediction-Dataset development by creating an account on GitHub. The dataset used in this project contains information about various health parameters of individuals, including: 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 Saved searches Use saved searches to filter your results more quickly Analysis based 4 different machine learning models. Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Please do let me know if you have any suggestions for the methods I'm using in this 3. 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. 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. Berdasarkan studi kasus dan karakteristik data target, metode yang akan digunakan adalah klasifikasi dengan Decision Tree. In this program, GaussianNB model is used for prediction and Python programming language. utils. For this purpose, I used the "healthcare-dataset-stroke-data" from Kaggle. The dataset consists of 11 clinical features which contribute to stroke occurence. Stroke Prediction w/ Machine Learning Classification Algorithms - ardasamett/Stroke-Prediction GitHub community articles Repositories. Symptom probabilities and weights are derived directly from textbooks like Harrison’s Principles and WHO reports, ensuring clinical relevance. As said above, there are 12 features with one target feature or response variable -stroke- and 11 explanatory variables. The best-performing model is deployed in a web-based application, with future developments including real-time data integration. - coderjones/stroke_prediction The Dataset_Stroke. age: Age of the patient. 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. 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. This R script is designed for comprehensive data analysis and model building using a Stroke dataset. ; sex: Gender (1 = Male, 0 = Female). - KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset Host and manage packages Security. ” Kaggle, 26 Jan. data. Contribute to Cvssvay/Brain_Stroke_Prediction_Analysis development by creating an account on GitHub. Contribute to AsifIkbal1/Healthcare-dataset-stroke-data-prediction development by creating an account on GitHub. - ajspurr/stroke_prediction Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. Input data is preprocessed and is given to over 7 models, where a maximum accuracy of 99. This package can be imported into any application for adding security features. Stroke Prediction This project goes through data exploration, cleaning and training of a neural network that uses entity embedding to map categorical variables. md at main · KSwaviman/EDA-Clustering-Classification-on Implemented Decision Trees, SVM, and KNN to predict stroke risk using a Kaggle dataset. 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. ; chol: Serum cholesterol (mg/dl). According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Data Dictionary Contribute to CTrouton/Stroke-Prediction-Dataset development by creating an account on GitHub. 4% is achieved. This involves using Python, deep learning frameworks like TensorFlow or Stroke is a medical condition that occurs when blood vessels in the brain are ruptured or blocked, resulting in brain damage. [ ] spark Gemini keyboard_arrow_down Data Dictionary. o Visualize the relation between stroke and other features by use pandas crosstab and seaborn heatmap. Stroke-Dataset-Prediction-and-EDA Penyakit stroke adalah penyakit gangguan peredaran darah ke otak akibat penyumbatan pembuluh darah atau pembuluh darah yang pecah. A dataset containing all the required fields to build robust AI/ML models to detect Stroke. Input Features: id: A unique identifier for each patient in the dataset. Manage code changes This dataset is designed for predicting stroke risk using symptoms, demographics, and medical literature-inspired risk modeling. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. Neural Network Model: We designed a feedforward neural network with the following According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. AI-powered developer platform Available add-ons. ) The data used in this notebook is a stroke prediction dataset. This project's outcome is to create a model that can predict whether Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. 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. 82 bmi #Conclusion: Reject the null hypothesis, finding that higher bmi level is likely 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. Each row in Prediction of stroke in patients using machine learning algorithms. We employ multiple machine learning and deep learning models, including Logistic Regression, Random Forest, and Keras Sequential models, to improve the prediction accuracy. You signed out in another tab or window. Each row in the data provides relavant information about the patient. Each row in Project Title: "Cerebral-Stroke-Prediction" for predicting whether a patient will suffer from a stroke, in order to provide timely interventions. Saved searches Use saved searches to filter your results more quickly 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 in We read every piece of feedback, and take your input very seriously. Stroke or not using the details on different features such as age, hypertension, average glucose level and so on. The output attribute is a Introduction¶ The dataset for this competition (both train and test) was generated from a deep learning model trained on the Stroke Prediction Dataset. Enterprise-grade security features “Stroke Prediction Dataset. 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. The dataset have: 4 numerical variables: "id", "age", "avg_glucose_leve" and "bmi" 8 categorical variables with 3 ordinal variables and 5 nominal variables: Using a Kaggle dataset to do a stroke prediction analysis. subdirectory_arrow_right 0 cells hidden spark Gemini This is a demonstration for a machine learning model that will give a probability of having a stroke. Stroke prediction is a critical area of research in healthcare, as strokes are one of the leading global causes of mortality (WHO: Top 10 Causes of Death). Sign in Product The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. This project builds a classifier for stroke prediction, which predicts the probability of a person having a stroke along with the key factors which play a major role in causing a stroke. Later tuned model by selecting variables with high coefficient > 0. - ragh4869/Stroke-Prediction-Analysis GitHub community articles Repositories. Find and fix vulnerabilities This program is developed to predict stroke in patients using Stroke Prediction Dataset. K-nearest neighbor and random forest algorithm are used in the dataset. Timely prediction and prevention are key to reducing its burden. The project is designed as a case study to apply deep learning concepts learned during the training period. Analyzing the dataset to get insights Hi! Welcome to the notebook! This notebook will serve as the step by step code explanation on how I analyse the stroke prediction dataset. Topics Trending Collections Enterprise Enterprise platform. - bishopce16/stroke_prediction_analysis Stroke Prediction Dataset created through R. Saved searches Use saved searches to filter your results more quickly Find and fix vulnerabilities Codespaces. The dataset contains 5110 observations - navidnaji/Stroke-prediction Feature Engineering; o Substituting the missing values with the mean. 47 - 2. · Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. The script includes data preparation, exploration, visualization, and the construction of prediction models. com This dataset is imbalenced . Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. Our work also determines the importance of the characteristics available and determined by the dataset. Both variants cause the brain to stop functioning Performing Various Classification Algorithms with GridSearchCV to find the tuned parameters - Akshay672/STROKE_PREDICTION_DATASET Analysis of stroke prediction dataset. The app is built using Streamlit, and it predicts the likelihood of a stroke based on real-life data. 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. age: The age You signed in with another tab or window. I have taken this dataset from kaggle. Feature distributions are close to, but not exactly the same, as the original. 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, This repository contains all the details about the Logistic Regression model that I made using the Stroke Prediction dataset from Kaggle According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Manage code changes According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. BMI Analysis: The mean and standard deviation of BMI were calculated for both males and females, providing insights into the health Saved searches Use saved searches to filter your results more quickly This GitHub repository contains the code for a Stroke Prediction App. Classification into 0 (no stroke) or 1 (stroke) Steps: Loading the dataset and required packages; Pre-processing data to convert character to numeric and to remove null values; Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. o scale values of Model Selection: Since this is a classification question, models include SVC, Logistic Regression, Random Forest, KNN and XGBooster are used. Advanced 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. The input variables are both numerical and categorical and will be explained below. Optimized dataset, applied feature engineering, and implemented various algorithms. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Penyakit stroke termasuk penyakit yang sangat ditakuti oleh kebanyakan orang di seluruh dunia, termasuk di Indonesia, karena bisa berakibat Predicting brain stroke by given features in dataset. You switched accounts on another tab or window. There are only 209 observation with stroke = 1 and 4700 observations with stroke = 0. Contribute to ksteve28/Stroke_Prediction development by creating an account on GitHub. AI-powered developer platform Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. - Stroke is a major cause of death in the United States every year. 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 Building and Deploying a Stroke Prediction Model in R using Random Forest and Shiny App - DrSwastika/Stroke-prediction-model-Random-forest 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 project predicts stroke disease using three ML algorithms - fmspecial/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. The goal of using an Ensemble Machine Learning model is to improve the performance of the model by combining the predictive powers of multiple models, which can Implementation of the study: "The Use of Deep Learning to Predict Stroke Patient Mortality" by Cheon et al. . If not available on GitHub, the notebook can be accessed on nbviewer, or alternatively on Kaggle. Fetching user details through web app hosted using Heroku. This repository contains the code and resources for building a deep learning solution to predict the likelihood of a person having a 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. Contribute to adnanhakim/stroke-prediction development by creating an account on GitHub. - skp163/Stroke_Prediction This dataset was imported, cleaned, and visualized. 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. ) Prediction probability: calculating the prediction probability for the test set. The goal is to, with the help of several easily measuable predictors such as smoking, hyptertension, age, to predict whether a person will suffer from a stroke. The dataset that was analyzed focused on a variety of factors and the relationships Machine learning project: classify and predict whether someone will have a stroke or not. DataSciencePortfolio Skip to content Stroke Prediction Dataset. Prediction of brain stroke based on imbalanced dataset in two machine learning algorithms, XGBoost and Neural Network To associate your repository with #Hypothesis: people who had stroke is higher in bmi than people who had no stroke. Contribute to Rasha-A21/Stroke-Prediction-Dataset development by creating an account on GitHub. The dataset consists of 303 rows and 14 columns. Stroke Prediction Dataset by using Machine Learning - AsifIkbal1/-Stroke-Prediction-Dataset Saved searches Use saved searches to filter your results more quickly We used as a dataset the "Stroke Prediction Dataset" from Kaggle. The rather simple neural network achieves approximately 98. Contribute to TomerSh135/Stroke-Prediction-Dataset development by creating an account on GitHub. The goal is to optimize classification performance while addressing challenges like imbalanced datasets and high false-positive rates in medical predictions. - GitHub - sa-diq/Stroke-Prediction: Prediction of stroke in patients using machine learning algorithms. Code Issues Pull requests DATA SCIENCE PROJECT ON STROKE PREDICTION- deployment link Navigation Menu Toggle navigation. The source code for how the model was trained and constructed can be We analyze a stroke dataset and formulate various statistical models for predicting whether a patient has had a stroke based on measurable predictors. Enterprise-grade security features Improving Stroke Risk Prediction and Prevention. This project utilizes the Stroke Prediction Dataset from Kaggle, available here. File Structure Stroke_Data. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. - JuanS286/StrokeClassifier. I used Logistic Regression with manual class weights since the dataset is imbalanced. You signed in with another tab or window. To run this project, you need to have Jupyter . Column Name Data Type Description; id Saved searches Use saved searches to filter your results more quickly 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 · GitHub is where people build software. csv. Instant dev environments Stroke Prediction Dataset. - enpure/kaggle--Binary-Classification-with-a-Tabular-Stroke-Prediction-Dataset · GitHub is where people build software. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. - Mahatir-Ahmed-Tusher/Stroke-Risk Contribute to sairamasharma10/Stroke_Prediction_Dataset development by creating an account on GitHub. Stroke Prediction Dataset. ; trestbps: Resting blood pressure (mm Hg). The following approach is used: Creating a data pipeline; Selecting the best models using cross-validation; Performing cross-validaition hyperparameter tuning on the best Stroke Prediction from kaggle dataset. The model here will help uncover patterns that are to increase risks of strokes helping people make better health decisions. The category "Other" was excluded due to the presence of only one observation. 2021, Retrieved September 10, 2022, 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 stroke detection project developed using R. py is inherited from torch. Star 0. Find and fix vulnerabilities Selected features using SelectKBest and F_Classif. Hi all, This is the capstone project on stroke prediction dataset. - FayD21/Capstone-2-Stroke-Prediction This project aims to predict the likelihood of stroke using a dataset from Kaggle that contains various health-related attributes. We are predicting the stroke probability using clinical measurements for a number of patients. Contribute to agauna-hdz/Stroke-Prediction-Dataset development by creating an account on GitHub. This contains a stroke dataset from kaggle which was used for predicting the possibility of a stroke, using Linear regression, SVM, and KNN About. Evaluated models, addressed overfitting, and documented the process in a Jupyter Notebook. The model built using sklearn's KNN module and uses the default settings. Write better code with AI Code review. I have done EDA, visualisation, encoding, scaling and modelling of dataset. 42 Explanatory Data Analysis -Patients between the age of 50-80 years old are at greater risk of getting a stroke. This suggests that the model was successful in correctly identifying a large proportion of the actual stroke cases in the dataset. The dataset used for this project is the Stroke Prediction Dataset from Kaggle. This underscores the need for early detection · 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. Analysis of the Stroke Prediction Dataset provided on Kaggle. georgemelrose / Stroke-Prediction-Dataset-Practice. 4. Speaking more about the dataset, the dataset consists of 5110 GitHub community articles Repositories. Each row in This data analysis aims to provide a comprehensive assessment of stroke, utilizing a health dataset containing various demographic and health information. Topics Trending Collections Pricing This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various Skip to content. Stroke Prediction can be done considering various features such as age, heart disease, smoking status, etc. 3 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. 2% classification accuracy via 5-fold cross validation approach. This system is used using amny of Machine Learning Algorithms like Logistic Regression, KNN Classifier, Random Forest, Support Vertor Machine and Naive Bayes Algorithms Write better code with AI Security. 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. Navigation Menu Toggle navigation. Acute Ischemic Stroke Prediction A machine learning approach for early prediction of acute ischemic strokes in patients based on their medical history. Using Strokes Prediction dataset dataset from kaggle, I made a binary classification model. Each row in the data provides relevan Synthetically generated dataset containing Stroke Prediction metrics. In this project, the National Health and Nutrition Examination Survey (NHANES) data from the National Center for Health Statistics (NCHS) is used to develop machine learning models. 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. #Create two table: stroke people, normal people #At 99% CI, the stroke people bmi is higher than normal people bmi at 0. 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. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', Data analysis on Dataset of patients who had a stroke (Sklearn, pandas, seaborn) - panosarv/stroke-prediction 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. Stroke Disease Prediction classifies a person with Stroke Disease and a healthy person based on the input dataset. 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 Synthetically generated dataset containing Stroke Prediction metrics. The value of true positive (TP) is emphasized because ideally we want to precisely predict the stroke. Instant dev environments According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. After imputing missing values with mean, I over-sampled the data as it was highly imbalanced. this project contains code for brain stroke prediction using public dataset, includes EDA, model training, and deploying using streamlit - samata18/brain-stroke-prediction analysis on a stroke dataset accompanied by machine learning algorithms to predict heart strokes. Dataset, thus can be exchanged with other datasets and loaders (At the moment there are two datasets with different transformations for training and validation). Feel free to use the original dataset as part of this competition The dataset used in the development of the method was the open-access Stroke Prediction dataset. AI-powered · More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. - GitHub - TomasJurkstas/stroke The "Cerebral Stroke Prediction" dataset is a real-world dataset used for the task of predicting the occurrence of cerebral strokes in individual. Our focus is on understanding how hypertension and average blood glucose levels interact with work type, potentially influencing stroke risk. Sign in The dataset used to build our model is Stroke Prediction Dataset which is available in Kaggle. Implemented and compared several classification models, including logistic regression, Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, and XGBoost. The model used for predictions is trained on a dataset of healthcare records. Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. This includes prediction algorithms which use "Healthcare stroke dataset" to predict the occurence of ischaemic heart disease. ; fbs: Fasting blood sugar > Saved searches Use saved searches to filter your results more quickly The outcome suggested a heavily imbalanced dataset as the accuracy was biased towards the "0" class as many samples in the datset were of no stroke potency. 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. In handling of this biased report, Synthetic Minority Oversampling Technique (SMOTE) model was deployed on the dataset to create a synthetic balance between both Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. - GitHub - acg12/stroke_prediction_ml: Machine learning project: classify and predict whether someone will have a stroke or not. 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 Contribute to ankitlehra/Stroke-Prediction-Dataset---Exploratory-Data-Analysis development by creating an account on GitHub. This project implements various neural network models to predict strokes using the Stroke Prediction Dataset from Kaggle. As per the WHO (World Health Organization) stroke is the 2nd leading cause of dead globally. About. 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. - Aroubb/Stroke-Prediction-using-Machine-Learning Balance dataset¶ Stroke prediction dataset is highly imbalanced. stroke_prediction_dataset_and_WorkBook In this folder the raw dataset and workbook in excel is given. This project aims to predict the likelihood of stroke in patients using various machine-learning techniques. - SwastikMo/STROKE_prediction. - SmNIslam03/stroke-prediction-analysis Host and manage packages Security. Resources Contribute to mnbpdx/stroke-prediction-dataset development by creating an account on GitHub. Stroke Predictions Dataset. A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. Contribute to ig-BaymaX/ACM-Stroke-Prediction development by creating an account on GitHub. Advanced Security. Our contribution can help predict early signs and prevention of this deadly disease - Write better code with AI Code review. ; Non-Linear Aging: . Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. Reload to refresh your session. 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 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. Setelah data diolah dan dirasa telah ideal, maka selanjutnya ialah membuat model machine learning dari dataset tersebut. ipynb, selects a model across many different classifiers and tunes the best selected classifiers using cross-validation. However, the low Stroke Predictor Dataset This project aims to predict whether an individual is at risk of having a stroke based on various demographic, lifestyle, and health-related factors. Contribute to sevesilvestre/StrokePredictionData development by creating an account on GitHub. 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 Resources Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. 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 Stroke and BMI have the strongest correlation with 0. Each row in the data provides relavant Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, This project employs a comprehensive dataset of relevant attributes to create a model that evaluates an individual's risk of stroke. Contribute to CTrouton/Stroke-Prediction-Dataset development by creating an account on GitHub. To determine which model is the best to Find and fix vulnerabilities Codespaces. Contribute to kushal3877/Stroke-Prediction-Dataset development by creating an account on GitHub. A. machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks Foreseeing the underlying risk factors of stroke is highly valuable to stroke screening and prevention. Each row represents a patient, and the columns represent various medical attributes. Initially an EDA has been done to understand the features and later Saved searches Use saved searches to filter your results more quickly Saved searches Use saved searches to filter your results more quickly About. Neural network to predict strokes. Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for Saved searches Use saved searches to filter your results more quickly Stroke prediction dataset. ; 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. I performed exploratory data analysis to get some insights from the data. ; cp: Chest pain type (0-3). In this case, I used SMOTE to oversample the minority class (stroke) to get a more balanced dataset. Impact: Stroke Prediction Dataset. xlsx: The primary dataset used in this analysis, containing variables relevant to stroke The dataset for this competition (both train and test) was generated from a deep learning model trained on the Stroke Prediction Dataset. Contribute to alyssahumpal/stroke_data development by creating an account on GitHub. Utilizing a dataset from Kaggle, we aim to identify significant factors that contribute to the likelihood of brain stroke occurrence. Saved searches Use saved searches to filter your results more quickly Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. Contribute to Eazlizy/stroke-prediction-and-prevention-dataset development by creating an account on GitHub. Contribute to emilyle91/stroke-prediction-dataset-analysis development by creating an account on GitHub. Chastity Benton 03/2022 [ ] spark Gemini keyboard_arrow_down Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. - NVM2209/Cerebral-Stroke-Prediction GitHub community articles Repositories. gender: The gender of the patient, which can be "Male" or "Female". 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. - Parisrossy/Stroke_Prediction Stroke prediction with machine learning and SHAP algorithm using Kaggle dataset - Silvano315/Stroke_Prediction Healthcare-dataset-stroke-data prediction. Performing EDA, data visualization, statistical inference, machine learning, model deployment. GitHub community articles Repositories. 7) An Exploratory Data Analysis on the Stroke Prediction Dataset to understand the various parameters affecting stroke and gain some insights on the same. - mriamft/Stroke-Prediction Saved searches Use saved searches to filter your results more quickly Clique aqui para realizar um pequeno teste! Pensamos que I. The dataset has been taken from Kaggle. Find and fix vulnerabilities About. Navigation Menu Toggle navigation This notebook, 2-model. - bahadobay/Stroke-Prediction Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. - rtriders/Stroke-Prediction Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. The analysis seeks to understand the relationships between patients' likelihood of having a stroke and other features, identify factors influencing stroke risk - Using a dataset of patient attributes to determine the likelihood of stroke.
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