Best stroke prediction dataset github. Prediction of brain stroke based on imbalanced dataset in .

Best stroke prediction dataset github We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction Dataset. 09 0. This project focuses on building a Brain Stroke Prediction System using Machine Learning algorithms, Flask for backend API development, and React. - hridaybasa/Stroke-Prediction-Using-Data-Science-And-Machine-Learning Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. 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. Version 2 significantly improves upon Version 1 by incorporating age-dependent symptom probabilities , gender-specific risk modifiers , and medically validated feature engineering . Our solution is to: Step 1) create a classification model to predict whether an Foreseeing the underlying risk factors of stroke is highly valuable to stroke screening and prevention. The following approach is used: #Hypothesis: people who had stroke is higher in bmi than people who had no stroke. You signed out in another tab or window. The dataset used to predict stroke is a dataset from Kaggle. core. 50 1176 1 0. ; The system uses a 70-30 training-testing split. Optimized dataset, applied feature engineering, and implemented various algorithms. The Dataset Stroke Prediction is taken in Kaggle. 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. You switched accounts on another tab or window. 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 Project Title: "Cerebral-Stroke-Prediction" for predicting whether a patient will suffer from a stroke, in order to provide timely interventions. Kaggle is an AirBnB for Data Scientists. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. heroku scikit-learn prediction stroke-prediction Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. Neural network to predict strokes. 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. gender: The gender of the patient, which can be "Male" or "Female". 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. md at main · KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset Contribute to HemantKumarRathore/STROKE-PREDICTION-using-multiple-ML-algorithem-and-comparing-best-accuracy-based-on-given-dataset development by creating an account You need to download ‘Stroke Prediction Dataset’ data using the library Scikit learn; ref is given below. The imbalanced classes created an uphill battle for the models. Cerebrovascular accidents (strokes) in 2020 were the 5th [1] leading cause of death in the United States. txt : File containing all required python librairies │ ├── run. 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. Achieved high recall for stroke cases. age: The age 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. Take it to the Real World: We need to use our model to make predictions using unseen data to see how it performs. 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 Selected features using SelectKBest and F_Classif. Most were overfit. The goal here is to get the best accuracy on a larger dataset. - msn2106/Stroke-Prediction-Using-Machine-Learning According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Leveraged skills in data preprocessing, balancing with SMOTE, and hyperparameter optimization using KNN and Optuna for model tuning. Prediction of brain stroke based on imbalanced dataset in ├── app │ ├── dataprocessing. - ankitlehra/Stroke-Prediction-Dataset---Exploratory-Data-Analysis The dataset contains 5110 unique records with 12 attributes for each, collecting from 2995 females and 2115 males. Dec 30, 2024 · The dataset consists of 303 rows and 14 columns. com This dataset is imbalenced . In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. Additionally, the project aims to analyze the dataset to identify the most significant features that contribute to stroke prediction. csv from the Kaggle Website, credit to the author of the dataset fedesoriano. 66 0. 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 represents a patient, and the columns represent various medical attributes. 98 0. Sep 18, 2024 · You signed in with another tab or window. 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 output attribute is a 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. ; The system uses Logistic Regression: Logistic Regression is a regression model in which the response variable (dependent variable) has categorical values such as True/False or 0/1. DataFrame'> Int64Index: 4908 entries, 0 to 5109 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 id 4908 non-null int64 1 gender 4908 non-null object 2 age 4908 non-null float64 3 hypertension 4908 non-null int64 4 heart_disease 4908 non-null int64 5 ever_married 4908 non-null object 6 work_type 4908 non-null object 7 Residence 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 The stroke occurrence distribution offers an unvarnished look at the dataset's balance and the stark contrast between stroke and non-stroke instances. Key Features: id: Unique identifier (dropped during preprocessing) gender: Male, Female, Other (categorical) age: Age in years (numerical) Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. 71 0. Dalam dataset tersebut terdapat beberapa fitur, diantaranya adalah: id: Untuk identifikasi setiap pasien. We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction dataset. This notebook, 2-model. A stroke occurs when the blood supply to a region of the brain is suddenly blocked or Easy Ensemble AdaBoost Classifier Balanced Accuracy Score: 0. Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. Timely prediction and prevention are key to reducing its burden. ; sex: Gender (1 = Male, 0 = Female). Predicted stroke risk with 92% accuracy by applying logistic regression, random forests, and deep learning on health data. 52 52 avg / total 0. 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. Fetching user details through web app hosted using Heroku. 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. - KSwaviman/EDA-Clustering-Classification-on-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 smoking status. py Overview: Membuat model machine learning yang memprediksi pengidap stroke berdasarkan data yang ada. The dataset consists of 11 clinical features which contribute to stroke occurence. So i used sampling technique to solve that problem. This dataset was created by fedesoriano and it was last updated 9 months ago. This dataset has been used to predict stroke with 566 different model algorithms. project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. Divide the data randomly in training and testing If not available on GitHub, the notebook can be accessed on nbviewer, or alternatively on Kaggle. Skip to content. Tools: Jupyter Notebook, Visual Studio Code, Python, Pandas, Numpy, Seaborn, MatPlotLib, Supervised Machine Learning Binary Classification Model, PostgreSQL, and Tableau. 51 1228 The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. With this in mind, we realized some kind of oversampling in our model could help with our unbalanced data set. 3 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. csv. Sign in Product As said above, there are 12 features with one target feature or response variable -stroke- and 11 explanatory variables. 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, gradientboost, xgboost with and without hyperpameter tunning I concluded, the Models performed above average. html and processes it, and uses it to make a prediction. Feature distributions are close to, but not exactly the same, as the original. In this project, I use the Heart Stroke Prediction dataset from WHO to predict the heart stroke. I used Logistic Regression with manual class weights since the dataset is imbalanced. Data Source: The healthcare-dataset-stroke-data. and choosign the best one (for this case): the With a relatively smaller dataset (although quite big in terms of a healthcare facility), every possible effort to minimize or eliminate overfitting was made, ranging from methods like k-fold cross validation to hyperparameter optimization (using grid search CV) to find the best value for each parameters in a model. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. - rtriders/Stroke-Prediction Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. Project description: According to WHO, stroke is the second leading cause of dealth and major cause of disability worldwide. In this case, I used SMOTE to oversample the minority class (stroke) to get a more balanced dataset. Recall is very useful when you have to Navigation Menu Toggle navigation. Navigation Menu Toggle navigation However, a deeper dive into the machine learning reports tells us that is not at all the case. - GitHub - Assasi 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. 76 0. 67 0. 7162480376766092 Predicted No Stroke Predicted Stroke Actual No Stroke 780 396 Actual Stroke 12 40 pre rec spe f1 geo iba sup 0 0. We get the conclusion that age, hypertension and work type self-employed would affect the possibility of getting stroke. Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for This project predicts stroke disease using three ML algorithms - fmspecial/Stroke_Prediction Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. The system uses data pre-processing to handle character values as well as null values. Dataset ini dibuat oleh seseorang dengan nama fedesoriano dan sudah diunggah ke situs kaggle mulai bulan februari 2021. An exploratory data analysis (EDA) and various statistical tests performed on a dataset focused on stroke prediction. . This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). com In this stroke prediction model we have implemented Logistic Regression, Random Forest & LightGBM. We have also done Hyperparameter tuning for each model. DataSet Description: The Kaggle stroke prediction dataset contains over 5 thousand samples with 11 total features (3 continuous More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. With just a few inputs—such as age, blood pressure, glucose levels, and lifestyle habits our advanced CNN model provides an accurate probability of stroke occurrence. Insight: The dataset presents a clear imbalance with a smaller proportion of stroke cases, challenging our model to learn from limited positive instances. Feel free to use the original dataset as part of this competition 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. 95 0. Toggle navigation Contribute to emilyle91/stroke-prediction-dataset-analysis development by creating an account on GitHub. Each row in the data provides relavant information about the patient. I have taken this dataset from kaggle. - mmaghanem/ML_Stroke_Prediction Performing Various Classification Algorithms with GridSearchCV to find the tuned parameters - Akshay672/STROKE_PREDICTION_DATASET This prediction model has been brought up for the purpose of predicting stroke cases in patients due to the increase in overall cases across the world. Mechine Learnig | Stroke Prediction. Contribute to anandj25/Heart-Stroke-Prediction development by creating an account on GitHub. To determine which model is the best to make stroke predictions, I plotte… Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. - NVM2209/Cerebral-Stroke-Prediction The project uses the "Healthcare Dataset Stroke Data" from Kaggle, containing 5,110 records with 12 features. I have done EDA, visualisation, encoding, scaling and modelling of dataset. Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. using visualization libraries, ploted various plots like pie chart, count plot, curves In our project, we want to predict stroke using machine learning classification algorithms, and evaluate and compare their results. Introduction¶ The dataset for this competition (both train and test) was generated from a deep learning model trained on the Stroke Prediction Dataset. #Create two table: stroke people, normal people #At 99% CI, the stroke people bmi is higher than normal people bmi at 0. 77 0. Later tuned model by selecting variables with high coefficient > 0. Stroke prediction with machine learning and SHAP algorithm using Kaggle dataset - Silvano315/Stroke_Prediction. 16 0. Brain strokes are a leading cause of disability and death worldwide. Resources This project implements various neural network models to predict strokes using the Stroke Prediction Dataset from Kaggle. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy. Stroke is a clinical syndrome that is characterized by signs and symptoms that indicate sudden or rapidly developing partial loss of brain function originating from cerebral vessels, can last for 24 hours or longer and may result in death. 47 - 2. Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. The dataset have: 4 numerical variables: "id", "age", "avg_glucose_leve" and "bmi" The dataset used in this project contains information about various health parameters of individuals, including: id: unique identifier; gender: "Male", "Female" or "Other" Prediction of stroke in patients using machine learning algorithms. age: Age of the patient. frame. For this purpose, I used the "healthcare-dataset-stroke-data" from Kaggle. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. [5] 2. The dataset used in the development of the method was the open-access Stroke Prediction dataset. Early prediction of stroke risk can help in taking preventive measures. ipynb, selects a model across many different classifiers and tunes the best selected classifiers using cross-validation. This dataset is designed for predicting stroke risk using symptoms, demographics, and medical literature-inspired risk modeling. py : File containing functions that takes in user inputs from home. js for the frontend. Data yang disediakan yaitu data train dan data test The aim of this project is to determine the best model for the prediction of brain stroke for the dataset given, to enable early intervention and preventive measures to reduce the incidence and impact of strokes, improving patient outcomes and overall healthcare. - GitHub - sa-diq/Stroke-Prediction: Prediction of stroke in patients using machine learning algorithms. GitHub repository for stroke prediction project. Resources <class 'pandas. 79 0. This project aims to use machine learning to predict stroke risk, a leading cause of long-term disability and mortality worldwide. Below is an overview of the dataset: Source: /backend/healthcare-dataset-stroke-data. If only 5% of our data set was in the "had stroke" category, the model could predict that no one would have a stroke and achieve 95% accuracy. Stroke are becoming more common among female than male; A person’s type of residence has no bearing on whether or not they have a stroke. py : File containing numerous data processing functions to transform our raw data frame into usable data │ ├── predict. Navigation Menu Toggle navigation You signed in with another tab or window. [ ] 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. Sign in Product Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. With the help of kaggle stroke prediction dataset, identify patients with a 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. Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. Data Preprocessing: This includes handling missing values, encoding categorical variables, dealing with outliers, and normalizing the data to prepare it for modeling. Contribute to kushal3877/Stroke-Prediction-Dataset development by creating an account on GitHub. This model is created with the following data in mind: patient data which includes medical history and demographic information. The analysis includes linear and logistic regression models, univariate descriptive analysis, ANOVA, and chi-square tests, among others. Dataset: Stroke Prediction Dataset To predict what factors influence a person’s stroke, I will utilize the stroke variable as the dependent variable. Learn more Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. The goal is to optimize classification performance while addressing challenges like imbalanced datasets and high false-positive rates in medical predictions. You signed in with another tab or window. Interestingly two of the stronger correlating factors to stroke, average glucose level and hypertension, were non-factors for prediction in the best model. machine-learning random-forest svm jupyter-notebook logistic-regression lda knn baysian stroke-prediction 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 Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. - ebbeberge/stroke-prediction Hi all, This is the capstone project on stroke prediction dataset. Reload to refresh your session. We intend to create a progarm that can help people monitor their risks of getting a stroke. The best-performing model is deployed in a web-based application, with future developments including real-time data integration. Intro: Worked with a team of 4 to perform analysis of the Kaggle Stroke Prediction Dataset using Random Forest, Decision Trees, Neural Networks, KNN, SVM, and GBM. Analysis of the Stroke Prediction Dataset provided on Kaggle. │ ├── requirements. To develop a model which can reliably predict the likelihood of a stroke using patient input information. The last column contains ‘1’ if the patient had stroke and ‘0’ if he or she hadn’t. Navigation Menu Toggle navigation. We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. - victorjongsoon/stroke-prediction Saved searches Use saved searches to filter your results more quickly Stroke Prediction Dataset. There are more female than male in the data set. 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. Contact Info Please direct all communications to Henry Tsai @ hawkeyedatatsai@gmail. - EDA-Clustering-Classification-on-Stroke-Prediction-Dataset/README. The "Cerebral Stroke Prediction" dataset is a real-world dataset used for the task of predicting the occurrence of cerebral strokes in individual. Input Features: id: A unique identifier for each patient in the dataset. The study uses a dataset with patient demographic and health features to explore the predictive capabilities of three algorithms: Artificial Neural Networks (ANN In this project, we used logistic regression to discover the relationship between stroke and other input features. How can this help patients in stroke prevention? Contribute to enot9910/Stroke-Prediction-Dataset development by creating an account on GitHub. 82 bmi #Conclusion: Reject the null hypothesis, finding that higher bmi level is likely Dataset ini saya ambil dari situs kaggle yang berjudul Stroke Predicition Dataset. The object is to use the best machine learning model and come back to study the correct predictions, and find out more precious characters on stroke patients. lnvytd qhaq itftih negv rxydf epow xyvcu woudg tfnkb hgd qtdqz vvxu zrcxtiy tuhce zogdfj