Stroke prediction dataset github python example o use SMOTE from Find and fix vulnerabilities Codespaces. Find and fix vulnerabilities Find and fix vulnerabilities Codespaces. Sign in Product Write better code with AI Security. This repository contains a Stroke Prediction project implemented in Python using machine learning techniques. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. It provides insights into various factors influencing stroke risk, allowing for binary classification (risk vs. It mostly consists of Python codes that I've been solving in my free time. Incorporate more data: To improve our dataset in the next iterations, we need to include more data points of people with stroke so that we can create target balance before modeling Navigation Menu Toggle navigation. 162 is just 4% of sample, however we will fill this null Stroke prediction project based on the kaggle stroke prediction dataset by Fedesoriano - kkalera/Stroke-Prediction. Sign in About. joblib │ │ └── optimized_stroke_model. csv │ │ └── stroke_data_final. csv. In this project, we replicate a research study Processed a dataset with patient information, handling missing values and predicting stroke potential with Random Forest - lrenek/Stroke-Prediction Project for the Master's Course of Machine Learning at University Hasselt & KU Leuven - VerbruggenD/ML_stroke_prediction Take it to the Real World: We need to use our model to make predictions using unseen data to see how it performs. data. Built as a personal project inspired by a YouTube tutorial, it showcases exploratory data analysis (EDA), predictive modeling, and web development 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. md at main · terickk/stroke-prediction-dataset Contribute to DAB-2021/Stroke-prediction-python development by creating an account on GitHub. o Visualize the relation between stroke and other features by use pandas crosstab and seaborn heatmap. Sign in A quick-start example to help you add the Syncfusion Flutter SignaturePad package to a Flutter app. In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. In this program, GaussianNB model is used for prediction and Python programming language. Jun 24, 2022 · For the purposes of this article, we will proceed with the data provided in the df variable. The model uses a dataset of patient records including demographic information, lifestyle habits, and medical history to predict the probability of having a stroke. Brain Stroke Prediction is an AI tool using machine learning to predict the likelihood of a person suffering from a stroke by analyzing medical history, lifestyle, and other relevant data. The app allows users to input relevant health and demographic details to predict the likelihood of having a stroke. 4. x = df. Project Overview: Dataset predicts stroke likelihood based on patient parameters (gender, age, diseases, smoking). Prediction of brain stroke based on imbalanced dataset in The KNDHDS dataset that the authors used might have been more complex than the dataset from Kaggle and the study’s neural network architecture might be overkill for it. Skip to content. Key features of the dataset include attributes related to various aspects of an individual's health, demographics Find and fix vulnerabilities Codespaces. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Contribute to meyram345/stroke_prediction development by creating an account on GitHub. In this project, I use the Heart Stroke Prediction dataset from WHO to predict the heart stroke. 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. py is inherited from torch. Contribute to haoyu-jia/Stroke-Prediction development by creating an account on GitHub. Before we proceed to build our machine learning model, we must begin with an exploratory data analysis that will allow us to find any inconsistencies in our data, as well as overall visualization of the dataset. Find and fix vulnerabilities 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. We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction dataset. This is my coding diary. the healthcare sector using Python. Execute the code in a Python environment. Leveraged skills in data preprocessing, balancing with SMOTE, and hyperparameter optimization using KNN and Optuna for model tuning. Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. The model uses a tensor with size (19. This was a project for the graduate course Applied Data Mining and Analytics in Business. Jun 2, 2021 · This is a Stroke Prediction Model. Hi all, This is the capstone project on stroke prediction dataset. Find and fix vulnerabilities Data Source: The healthcare-dataset-stroke-data. Optimized dataset, applied feature engineering, and implemented various algorithms. 1) as input, which contains data from the data set and classifies whether a patient is more likely to have a stroke using multi-category classification The classification is done using the softmax activation function which calculates the probability of the input tensor This project aims to build a stroke prediction model using Python and machine learning techniques. PREDICTION-STROKE/ ├── data/ │ ├── models/ │ │ ├── best_stroke_model. o Replacing the outlier values with the mode. 7) machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks tensor kaggle-dataset stroke-prediction Updated Mar 30, 2022 Python 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. . 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. It contains data for upto 6 mental imageries primarily for the motor movements. - mmaghanem/ML_Stroke_Prediction 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. Navigation Menu Toggle navigation This project is about stroke prediction in individuals, analyzed through provided dataset from kaggle. - eazziz/Stroke-Prediction-Using-Machine-Learning Machine Learning Model as Python Package "stroke-pred-p0w11' Data Storage unit using PostgresSQl & Sqlalchmey Data Ingestion job using Airflow to collect our data based on the user inputs. Interestingly two of the stronger correlating factors to stroke, average glucose level and hypertension, were non-factors for prediction in the best model. utils. Kaggle is an AirBnB for Data Scientists. The goal of this project is to predict the likelihood of a person having a stroke based on various demographic, lifestyle, and medical factors. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. The application combines a machine learning model trained on a healthcare dataset with a Flask backend and a React frontend, providing an accessible tool for users to assess their stroke probability. Exploratory Data Analysis. The app is built using Streamlit, and it predicts the likelihood of a stroke based on real-life data. Instant dev environments One dataset after value conversion. This dataset has been used to predict stroke with 566 different model algorithms. Predicting whether a patient is likely to get stroke or not - stroke-prediction-dataset/README. Resources 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. python java api machine-learning machine-learning-algorithms android-application logistic-regression android-studio android-app flask-api kaggle-dataset volley-library numpy-library sklearn-library heart-disease-predictor heart-disease-prediction This project aims to build a stroke prediction model using Python and machine learning techniques. csv The KNDHDS dataset that the authors used might have been more complex than the dataset from Kaggle and the study’s neural network architecture might be overkill for it. 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 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. Once the code is running, you can access the FastAPI endpoints for predictions. o scale values of avg_glucose_level, bmi, and age by using StandardScaler in sklearn. The API can be integrated seamlessly into existing healthcare systems Skip to content. Navigation Menu Toggle navigation The dataset used to predict stroke is a dataset from Kaggle. Machine Learning Model as Python Package "stroke-pred-p0w11' Data Storage unit using PostgresSQl & Sqlalchmey Data Ingestion job using Airflow to collect our data based on the user inputs. The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. Standard codes for the stroke data: synthea-stroke-dataset-codes. ipynb at main · jaewon4067/Codes_with_Python Skip to content. Python classifier models LogisticRegression, MLPClassifier, DecisionTreeClassifier and RandomForestClassifier were used for the data training and prediction. The aim of this project is to predict the probability of having a stroke using a dataset from Kaggle. Instant dev environments Practice with imbalanced datasets. 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. Initially an EDA has been done to understand the features and later 3. Tools: Jupyter Notebook, Visual Studio Code, Python, Pandas, Numpy, Seaborn, MatPlotLib, Supervised Machine Learning Binary Classification Model, PostgreSQL, and Tableau. The imbalanced classes created an uphill battle for the models. Data has null value: BMI column has 162 null values. - ajspurr/stroke_prediction Stroke has a serious impact on individuals and healthcare systems, making early prediction crucial. - ansonnn07/stroke-prediction By making the repository available on GitHub, the author promotes collaboration and encourages others to contribute to the project. csv Realtime health care dataset* and contains records of 5110 patients with 12 attributes Write better code with AI Security. Mental-Imagery Dataset: 13 participants with over 60,000 examples of motor imageries in 4 interaction paradigms recorded with 38 channels medical-grade EEG system. The goal of this project is to build a model with an accuracy of 93% to predict stroke. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. Contribute to DejasDejas/Stroke_Prediction_Python development by creating an account on GitHub. Navigation Menu Toggle navigation. For the process, the stroke dataset was splitted in training and testing datasets in 80/20 rate. Predicting whether a person suffers from stroke using Machine Learning. Dataset: Stroke Prediction Dataset The Dataset Stroke Prediction is taken in Kaggle. 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 visualization libraries, ploted various plots like pie chart, count plot, curves Contribute to singli-h/Stroke-Prediction-using-Python development by creating an account on GitHub. using visualization libraries, ploted various plots like pie chart, count plot, curves 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. Deployment and API: The stroke prediction model is deployed as an easy-to-use API, allowing users to input relevant health data and obtain real-time stroke risk predictions. - GitHub - zeal-git/StrokePredictionModel: This project is about stroke prediction in individuals, analyzed through provided dataset from kaggle. Sign in Product The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. drop(['stroke'], axis=1) y = df['stroke'] 12. Analysis of the Stroke Prediction Dataset to provide insights for the hospital. This model focuses on stroke prediction in humans with respect to parsing of dataset filled with individual based chararcterstics. The dataset consists of 11 clinical features which contribute to stroke occurence. The goal of this ML model is to figure out if a person will experience a stroke on the basis of age, nature of work, urban/rural residency, marital status, and several clinical parameters. It’s a crowd- sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science, machine learning and predictive analytics problems. csv │ └── raw/ │ └── healthcare-dataset According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Stroke ML datasets from 30k to 150k Synthea patients, available in Harvard Dataverse: Synthetic Patient Data ML Dataverse. Stroke_Prediction model for DSTI python labs project What this project is for The objective of this project was to train a machine learning model to predict whether a patient had a stroke or not, using a data set of 5110 patients. Navigation Menu Toggle navigation Write better code with AI Security. By analyzing medical and lifestyle-related data, the model helps identify individuals at risk of stroke. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). o Convert categorical variables to numbers by LabelEncoder in sklearn. You will learn how to add a SignaturePad widget to a Flutter project, set the background color, stroke color, and stroke width, and clear the signature in the Signature Pad. Predicted stroke risk with 92% accuracy by applying logistic regression, random forests, and deep learning on health data. 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. Each row in the data provides relavant information about the patient. It uses the Stroke Prediction Dataset found on Kaggle. Find and fix vulnerabilities Aug 25, 2022 · This project hence helps to predict the stroke risk using prediction model and provide personalized warning and the lifestyle correction message. Instant dev environments Model comparison techniques are employed to determine the best-performing model for stroke prediction. joblib │ │ ├── model_metadata. Impact: Find and fix vulnerabilities Codespaces. ) Prediction probability: calculating the prediction probability for the test set. machine-learning random-forest svm jupyter-notebook logistic-regression lda knn baysian stroke-prediction The dataset used in the development of the method was the open-access Stroke Prediction dataset. Instant dev environments Write better code with AI Security. Automate any workflow In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. - Codes_with_Python/Stroke Prediction Dataset. Each part has its target feature -stroke- and explanatory features. Find and fix vulnerabilities Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. Feature Selection: The web app allows users to select and analyze specific features from the 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. The competition provides a synthetic dataset that was generated from a deep learning model trained on the Stroke Prediction Dataset. Navigation Menu Toggle navigation Machine Learning Model as Python Package "stroke-pred-p0w11' Data Storage unit using PostgresSQl & Sqlalchmey Data Ingestion job using Airflow to collect our data based on the user inputs. In handling of this biased report, Synthetic Minority Oversampling Technique (SMOTE) model was deployed on the dataset to create a synthetic balance between both classes of output. This model runs with a prediction accuracy of 82%. A stroke occurs when the brain gets damaged as a result of interruption of the blood supply. Data analysis on Dataset of patients who had a stroke (Sklearn, pandas, seaborn) - panosarv/stroke-prediction Find and fix vulnerabilities Codespaces. A Data Science project which predicts stroke using python - pelinsugok/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 This is a simple dense neural network created with keras running on top of tensorflow. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. Jun 12, 2024 · This code provides the Matlab implementation that detects the brain tumor region and also classify the tumor as benign and malignant. 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. With the growing use of technology in medicine, electronic health records (EHR) provide valuable data for improving diagnosis and patient management. Anto, "Tumor detection and classification of MRI brain image using wavelet transform and SVM", 2017 International Conference on Signal Processing and Communic… This GitHub repository contains the code for a Stroke Prediction App. The Stroke Risk Prediction Dataset is a comprehensive dataset designed for machine learning and medical research purposes. This dataset was created by fedesoriano and it was last updated 9 months ago. Mathew and P. Contribute to CTrouton/Stroke-Prediction-Dataset development by creating an account on GitHub. id: Patient ID; gender: "Male", "Female" or "Other" age: patient age; hypertension: 0 if the patient does not have hypertension, 1 if the patient does not have hypertension; heart_disease: 0 if the patient does not have heart disease, 1 if the patient has A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. - bpalia/StrokePrediction Fonte: Data for: A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical-datasets Análise exploratória da base de dados Visualização da base, levantamento de perguntas, tratamento da base, tratamento de outliers Deskripsi; Dataset: Stroke Prediction Dataset: Masalah: Berdasarkan latar belakang di atas, dapat kita ketahui bahwa penyakit stroke adalah masalah kesehatan yang cukup serius, terutama bagi orang yang sudah cukup berumur, memiliki beberapa penyakit penyebab stroke, dan yang paling umum adalah karena memiliki kebiasaan merokok. These datasets were used to simulate ML-LHS in the Nature Sci Rep paper. Contribute to Vikram3003/Stroke-Analysis-and-Prediction-Python development by creating an account on GitHub. Contribute to nevetto/Stroke_predictions development by creating an account on GitHub. The objective of the application is to show a sequential model to be trained with various activation neural layers. This program is developed to predict stroke in patients using Stroke Prediction Dataset. Find and fix vulnerabilities Most were overfit. Dec 28, 2024 · Write better code with AI Security. no risk) and regression (risk percentage prediction). Dataset, thus can be exchanged with other datasets and loaders (At the moment there are two datasets with different transformations for training and validation). Find and fix vulnerabilities Actions. Stroke Prediction Dataset. Contribute to sxu75374/Heart-Stroke-Prediction development by creating an account on GitHub. Stroke is a disease that affects the arteries leading to and within the brain. Instant dev environments Contribute to anandj25/Heart-Stroke-Prediction development by creating an account on GitHub. Toggle navigation. GitHub is where people build software. Stroke Prediction Using Machine Learning (Classification use case) Topics machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction Write better code with AI Security. The model preprocesses data, handles class imbalance, and utilizes Logistic Regression and K-Nearest Neighbors (KNN) algorithms to provide predictions. This proof-of-concept application is designed for educational purposes and should not be used for medical advice. csv from the Kaggle Website, credit to the author of the dataset fedesoriano. Find and fix vulnerabilities Dec 11, 2022 · This project hence helps to predict the stroke risk using prediction model and provide personalized warning and the lifestyle correction message. - SmNIslam03/stroke-prediction-analysis Basado en O'reilly/ Introduction to machine learning with python - Algoritms_Intro_machineLearningWithPython/Stroke Prediction Dataset. Achieved high recall for stroke cases. Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Users can offer suggestions, provide enhancements, and even propose alternative models or approaches to improve the stroke prediction system to fuel advancements in stroke prediction research. How can this help patients in stroke prevention? Age is the strongest stroke indicator. Find and fix vulnerabilities Actions I have considered the problem of predicting the chances of a patient having a stroke, and for this, I have used healthcare dataset from Kaggle. Sign in Product Feature Engineering; o Substituting the missing values with the mean. For example, the KNDHDS dataset has 15,099 total stroke patients, specific regional data, and even has sub classifications for which type of stroke the patient had. Dependencies Python (v3. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. Instant dev environments Code and Datasets for the paper "An Interpretable Risk Prediction Model for Healthcare with Pattern Attention", published on BMC Medical Informatics and Decision Making. About. [ ] Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. It gives users a quick understanding of the dataset's structure. Find and fix vulnerabilities Codespaces. Automate any workflow Packages Sep 15, 2022 · We set x and y variables to make predictions for stroke by taking x as stroke and y as data to be predicted for stroke against x. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. The input variables are both numerical and categorical and will be explained below. For example, the higher values for age impact the decision-making of the model towards the stroke prediction and for high average glucose level the model tends to predict the patients with stroke and it has a greater impact respect to the lower values. I have done EDA, visualisation, encoding, scaling and modelling of dataset. Our task is to predict whether a patient will suffer a stroke or not given the medical data of that patient. main This project aims to predict the likelihood of a stroke using machine learning techniques on a healthcare dataset. This project predicts whether someone will have a stroke or not - jovyinny/Stroke-Prediction Navigation Menu Toggle navigation. Data The dataset specified in data. synthea-pt30k-stroke-ml-table-sel-convert. Early intervention and preventive measures can be taken to reduce the likelihood of stroke occurrence, potentially saving lives and improving the quality of life for patients. Instant dev environments Dataset:: Stroke Prediction Dataset from Kaggle website Kaggle Dataset 1 Kaggle Dataset 2. Data Analysis – Explore and visualize data to understand stroke-related factors. electronic-health-record sepsis interpretable-deep-learning mortality-prediction pattern-attention value-embedding Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras. Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. Dataset: The dataset used for this project is sourced from *healthcare-dataset-stroke-data. The output attribute is a More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Recall is very useful when you have to Write better code with AI Security. The model used for predictions is trained on a dataset of healthcare records. GitHub community articles healthcare-dataset-stroke-data. ) The data used in this notebook is a stroke prediction dataset. The model here will help uncover patterns that are to increase risks of strokes helping people make better health decisions. Objective: Create a machine learning model predicting patients at risk of stroke. 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. csv │ │ ├── stroke_data_engineered. On this dataset, I have first performed Preprocessing and Visualization, after which I have carried out feature selection. By doing so, it also urges medical users to strengthen the motivation of health management and induce changes in their health behaviors. joblib │ ├── processed/ │ │ ├── processed_stroke_data. Install the required libraries mentioned in the code, such as pandas, scikit-learn, xgboost, seaborn, fastapi, and colabcode. ipynb at master · jeansyo/Algoritms_Intro_machineLearningWithPython 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. Data This project utilizes the Stroke Prediction Dataset from Kaggle, available here. The dataset was adjusted to only include adults (Age >= 18) because the risk factors associated with stroke in adolescents and children, such as genetic bleeding disorders, are not captured by this dataset. GitHub repository for stroke prediction project. This code is implementation for the - A. The dataset for this project originates from the Kaggle Playground Series, Season 3, Episode 2. deep-neural-networks timeseries deep-learning keras lstm deep-learning-algorithms keras-models keras-neural-networks lstm-neural-networks prediction-model keras-tensorflow predictive-maintenance Stroke prediction using python ML models. You will also learn how to save and open the signature as an image in mob… The Stroke Risk Prediction Dataset is a comprehensive dataset designed for machine learning and medical research purposes. vxx gjspbo serip cwwembaa kelww zrwsy fcucwdsgc axol sykso jnluop egopnv hmrgex xnhw bzpzbylka nfoqe