Best free stroke prediction dataset github Among the records, 1. Dataset yang Write better code with AI Code review. 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 kushal3877/Stroke-Prediction-Dataset development by creating an account on GitHub. - GitHub - erma0x/stroke-prediction-model: Data exploration, preprocessing, analysis and building a stroke model prediction in the life of the patient. [ ] 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. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', 'Work Type Navigation Menu Toggle navigation. Feature Selection: The web app allows users to select and analyze specific features from the dataset. It gives users a quick understanding of the dataset's structure. They offer relatively clean data, well-suited for machine learning tasks, with an abundance of variables to aid in making predictions for the target column. o scale values of avg_glucose_level, bmi, and age by using StandardScaler in sklearn. Sign in Product 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. joblib │ ├── processed/ │ │ ├── processed_stroke_data. We aim to identify the factors that con Navigation Menu Toggle navigation. 3. In the code, we have created the instance of the Flask() and loaded the model. csv │ └── raw/ │ └── healthcare-dataset <class 'pandas. - NVM2209/Cerebral-Stroke-Prediction Write better code with AI Code review. The objective is to predict brain stroke from patient's records such as age, bmi score, heart problem, hypertension and smoking practice. gender: "Male", "Female" or "Other" age: age of the patient. Dependencies Python (v3. Find and fix vulnerabilities Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. We intend to implement a prototype that senses relevant parameters and need not necessarily be wearable The Stroke Risk Prediction Dataset is a comprehensive dataset designed for machine learning and medical research purposes. Feature distributions are close to, but not exactly the same, as the original. Analysis of the Stroke Prediction Dataset to provide insights for the hospital. Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for Introduction¶ The dataset for this competition (both train and test) was generated from a deep learning model trained on the Stroke Prediction Dataset. We have also done Hyperparameter tuning for each model. 92 32. Resources Contribute to arturnovais/Stroke-Prediction-Dataset development by creating an account on GitHub. Manage code changes The Nencki-Symfonia EEG/ERP dataset: high-density electroencephalography (EEG) dataset obtained at the Nencki Institute of Experimental Biology from a sample of 42 healthy young adults with three cognitive tasks: (1) an extended Multi-Source Interference Task (MSIT+) with control, Simon, Flanker, and multi-source interference trials; (2) a 3 Stroke and BMI have the strongest correlation with 0. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Later tuned model by selecting variables with high coefficient > 0. model --lrsteps 200 250 --epochs 300 --outbasepath ~/tmp/shape --channelscae 1 16 24 32 100 200 1 --validsetsize 0. Prediction of brain stroke based on imbalanced dataset in 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. Navigation Menu Toggle navigation To predict what factors influence a person’s stroke, I will utilize the stroke variable as the dependent variable. Since the dataset is small, the training of the entire neural network would not provide good results so the concept of Transfer Learning is used to train the model to get more accurate resul Saved searches Use saved searches to filter your results more quickly AUC-PR: The Neural Network model has a slightly higher AUC-PR score (0. ipynb contains the model experiments. Activate the above environment under section Setup. 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. Contribute to SourcM/health-care-dataset development by creating an account on GitHub. Version 2 significantly improves upon Version 1 by incorporating age-dependent symptom probabilities , gender-specific risk modifiers , and medically validated feature engineering . Achieved high recall for stroke cases. You switched accounts on another tab or window. 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 Stroke prediction. I used a KNN to make the Stroke predictions. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. These datasets were used to simulate ML-LHS in the Nature Sci Rep paper. Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. 3). Tools: Jupyter Notebook, Visual Studio Code, Python, Pandas, Numpy, Seaborn, MatPlotLib, Supervised Machine Learning Binary Classification Model, PostgreSQL, and Tableau. 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. One dataset after value conversion. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. There are more female than male in the data set. Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. 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. csv Synthetically generated dataset containing Stroke Prediction metrics. This video showcases the functionality of the Tkinter-based GUI interface for uploading CT scan images and receiving predictions on whether the image indicates a brain stroke or not. joblib │ │ └── optimized_stroke_model. Predicted stroke risk with 92% accuracy by applying logistic regression, random forests, and deep learning on health data. hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension Stroke Prediction Dataset. html is pressed) and converts it into an array. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). o Replacing the outlier values with the mode. Future Direction: Incorporate additional types of data, such as patient medical history, genetic information, and clinical reports, to enhance the predictive accuracy and reliability of the model. With over 9 years in the data analysis field, I am currently pursuing a degree in this area to further my skills and knowledge. Stroke Prediction Dataset If not available on GitHub, the notebook can be accessed on nbviewer, or alternatively on Kaggle. As said above, there are 12 features with one target feature or response variable -stroke- and 11 explanatory variables. Kaggle is an AirBnB for Data Scientists. A dataset containing all the required fields to build robust AI/ML models to detect Stroke. using visualization libraries, ploted various plots like pie chart, count plot, curves Stroke Disease Prediction classifies a person with Stroke Disease and a healthy person based on the input dataset. I have a passion for investigating and analysing data. csv from the Kaggle Website, credit to the author of the dataset fedesoriano. machine-learning random-forest svm jupyter-notebook logistic-regression lda knn baysian stroke-prediction cerebral stroke prediction based on imbalanced medical dataset - Jdss026/stroke-classifier. Contribute to TomerSh135/Stroke-Prediction-Dataset development by creating an account on GitHub. The dataset have: 4 numerical variables: "id", "age", "avg_glucose_leve" and "bmi" 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. We get the conclusion that age, hypertension and work type self-employed would affect the possibility of getting stroke. Fetching user details through web app hosted using Heroku. and choosign the best one (for this case): the Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. Each row in the data provides relavant information about the patient. 5% of them are related to non-stroke patients. csv │ │ ├── stroke_data_engineered. ; Recall: The ability of the model to capture actual positive instances. Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. This package can be imported into any application for adding security features. The goal is to develop a robust model for stroke prediction. drop(['stroke'], axis=1) y = df['stroke'] 12. Timely prediction and prevention are key to reducing its burden. 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. 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 Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. I have done EDA, visualisation, encoding, scaling and modelling of dataset. o Visualize the relation between stroke and other features by use pandas crosstab and seaborn heatmap. The whole code is built on different Machine learning techniques and built on website using Django machine-learning django random-forest logistic-regression decision-trees svm-classifier knn-classification navies-bayes-classifer heart-disease-prediction kidney-disease-prediction You signed in with another tab or window. For modeling, the categorical features where encoded, XGBoost was use das Predicting whether a patient is likely to get stroke or not - stroke-prediction-dataset/code. . 7) 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. The project aims at displaying the charts/plots of the number of people affected by stroke based on the input parameters like smoking status, high blood pressure level, Cholesterol level, obesity level in some of the countries. This dataset documents rates and trends in heart disease and stroke mortality. AUC-PR measures the area under the precision-recall curve and provides an aggregate measure of model performance for imbalanced datasets. frame. 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. 21 N/A never smoked 1 Male 80 0 1 Yes Private Rural 105. Stacking. The best-performing model is deployed in a web-based application, with future developments including real-time data integration. 42 Explanatory Data Analysis -Patients between the age of 50-80 years old are at greater risk of getting a stroke. Instant dev environments Write better code with AI Security. Optimized dataset, applied feature engineering, and implemented various algorithms. synthea-pt30k-stroke-ml-table-sel-convert. - Issues · KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. DataFrame'> Int64Index: 4088 entries, 25283 to 31836 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 gender 4088 non-null object 1 age 4088 non-null float64 2 hypertension 4088 non-null int64 3 heart_disease 4088 non-null int64 4 ever_married 4088 non-null object 5 work_type 4088 non-null object 6 Residence_type 4088 non-null Navigation Menu Toggle navigation. Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. Issues are used to track todos, bugs, feature requests, and more. Feature Engineering; o Substituting the missing values with the mean. csv. 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. model. Our model peformed amazingly having a recall of 100% meaning that our model can predict 100% of the time patients with stroke. Sign in Product 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. Contact Info Please direct all communications to Henry Tsai @ hawkeyedatatsai@gmail. 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. - GitHub - Assasi Model comparison techniques are employed to determine the best-performing model for stroke prediction. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. Code in this repository is used for testing of methods for predicting heat stroke with a wearable monitor. Mechine Learnig | Stroke Prediction. We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. Write better code with AI Security. Navigation Menu Toggle navigation. In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. csv │ │ └── stroke_data_final. - bpalia/StrokePrediction. 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. Contribute to orkunaran/Stroke-Prediction development by creating an account on GitHub. - hridaybasa/Stroke-Prediction-Using-Data-Science-And-Machine-Learning The system uses data pre-processing to handle character values as well as null values. Sep 21, 2021 · <class 'pandas. 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. Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. Stroke prediction with machine learning and SHAP algorithm using Kaggle dataset - Silvano315/Stroke_Prediction. Specifically, this report presents county (or county equivalent) estimates of heart Write better code with AI Code review. We used as a dataset the "Stroke Prediction Dataset" from Kaggle. The dataset includes 100k patient records. to make predictions of stroke cases based on simple health 11 clinical features for predicting stroke events Stroke Prediction Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. o use SMOTE from The Jupyter notebook notebook. id: unique identifier. 9. Marital status and presence of heart disease have no significant effect on stroke; Older age, hypertension, higher glucose level and higher BMI increase the risk of stroke Contribute to arturnovais/Stroke-Prediction-Dataset development by creating an account on GitHub. o Convert categorical variables to numbers by LabelEncoder in sklearn. 4% is achieved. Nov 21, 2023 · Didn’t eliminate the records due to dataset being highly skewed on the target attribute – stroke and a good portion of the missing BMI values had accounted for positive stroke; The dataset was skewed because there were only few records which had a positive value for stroke-target attribute Stroke Prediction Using Machine Learning (Classification use case) Topics machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction Skip to content. Feel free to use the original dataset as part of this competition PREDICTION-STROKE/ ├── data/ │ ├── models/ │ │ ├── best_stroke_model. As issues are created, they’ll appear here in a searchable and filterable list. It also has a precision of 100% meaning our model can predict 100% of the time patients that don't have stroke A ML model which predicts the probability of a person having a stroke along with the key factors which play a major role in causing a stroke. Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. The "Cerebral Stroke Prediction" dataset is a real-world dataset used for the task of predicting the occurrence of cerebral strokes in individual. where P k, c is the prediction or probability of k-th model in class c, where c = {S t r o k e, N o n − S t r o k e}. Apr 17, 2021 · 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. Instant dev environments Part I (see Stroke prediction using Logistic regression. Analysis of the Stroke Prediction Dataset provided on Kaggle. 1906) compared to the XGBoost model (0. Mar 22, 2023 · gender age hypertension heart_disease ever_married work_type Residence_type avg_glucose_level bmi smoking_status stroke Female 61 0 0 Yes Self-employed Rural 202. Manage code changes Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. Dealing with Class Imbalance. 1545). By doing so, it also urges medical users to strengthen the motivation of health management and induce changes in their health behaviors. 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. - ajspurr/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. This dataset has been used to predict stroke with 566 different model algorithms. - JuanS286/StrokeClassifier This project looks to create a stroke classifier to predict the likelihood of a patient to have a stroke. 3 The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. The dataset was obtained from Kaggle and contains 5110 rows and 10 columns to detect stroke predictions. Manage code changes Skip to content Find and fix vulnerabilities Codespaces. For learning the shape space on the manual segmentations run the following command: train_shape_reconstruction. - GitHub - sa-diq/Stroke-Prediction: Prediction of stroke in patients using machine learning algorithms. - mmaghanem/ML_Stroke_Prediction 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. ipynb at main · terickk/stroke-prediction-dataset Write better code with AI Code review. Prediksi penyakit stroke ini merupakan suatu sistem yang mampu melakukan prediksi yang dapat membantu dalam menilai apakah seorang pasien memiliki atau tidaknya risiko yang signifikan untuk mengidap penyakit stroke, dan hal ini didasarkan pada data medis yang tersedia atau diberikan. Contribute to renjinirv/Stroke-prediction-dataset development by creating an account on GitHub. Sign in Product This is a repository for code used in Bioengineering Capstone at Stanford (Bioe 141A/B). GitHub Copilot. Analysis based 4 different machine learning models. 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. Manage code changes 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, presence of diseases, and smoking status. Manage code changes The dataset used in this project contains information about various health parameters of individuals, including: id: unique identifier; gender: "Male", "Female" or "Other" Welcome to my portfolio. Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. The dataset used in the development of the method was the open-access Stroke Prediction dataset. - sakshitodi/Stroke-Prediction Dec 11, 2022 · This project hence helps to predict the stroke risk using prediction model and provide personalized warning and the lifestyle correction message. ipynb - 4. 5 never smoked 1 Aug 25, 2022 · More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. Manage code changes Contribute to CTrouton/Stroke-Prediction-Dataset development by creating an account on GitHub. Leveraged skills in data preprocessing, balancing with SMOTE, and hyperparameter optimization using KNN and Optuna for model tuning. Using SQL and Power BI, it aims to identify trends and corr (Sorry about that, but we can’t show files that are this big right now app. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. Reload to refresh your session. GitHub repository for stroke prediction project. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. - NIRMAL1508/STROKE-DISEASE-PREDICTION Whether your focus is on predictions or classification, these datasets are not only intriguing but also invaluable for machine learning endeavors. 3 --fold 17 6 2 26 11 4 1 21 16 27 24 18 9 22 12 0 3 8 23 25 7 10 19 Selected features using SelectKBest and F_Classif. using visualization libraries, ploted various plots like pie chart, count plot, curves 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. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. Stroke Prediction Dataset. Input data is preprocessed and is given to over 7 models, where a maximum accuracy of 99. x = df. Dataset: Stroke Prediction Dataset Prediction of stroke in patients using machine learning algorithms. This dataset is designed for predicting stroke risk using symptoms, demographics, and medical literature-inspired risk modeling. Initially an EDA has been done to understand the features and later More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Stroke ML datasets from 30k to 150k Synthea patients, available in Harvard Dataverse: Synthetic Patient Data ML Dataverse. core. It provides insights into various factors influencing stroke risk, allowing for binary classification (risk vs. Stroke prediction project based on the kaggle stroke prediction dataset by Fedesoriano - kkalera/Stroke-Prediction. heroku scikit-learn prediction stroke-prediction Predicting whether a patient is likely to get stroke or not - stroke-prediction-dataset/README. 5% of them are related to stroke patients and the remaining 98. We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a 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. Data exploration, preprocessing, analysis and building a stroke model prediction in the life of the patient. ; The system uses a 70-30 training-testing split. The API can be integrated seamlessly into existing healthcare systems project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. The output attribute is a 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. Model performance was evaluated using several metrics suited for imbalanced datasets: Precision: The accuracy of positive predictions. It includes data preprocessing (label encoding, KNN imputation, SMOTE for balancing), and trains models like Naive Bayes, Decision Tree, SVM, and Logistic Regression. Additionally, the project aims to analyze the dataset to identify the most significant features that contribute to stroke prediction. 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 Hi all,. To determine which model is the best to make stroke predictions, I plotte… Find and fix vulnerabilities Codespaces. I used Logistic Regression with manual class weights since the dataset is imbalanced. Using SQL and Power BI, it aims to identify trends and corr This project predicts stroke disease using three ML algorithms - fmspecial/Stroke_Prediction The Dataset Stroke Prediction is taken in Kaggle. joblib │ │ ├── model_metadata. Contribute to Rasha-A21/Stroke-Prediction-Dataset development by creating an account on GitHub. md at main · terickk/stroke-prediction-dataset The dataset used to predict stroke is a dataset from Kaggle. Hi all, This is the capstone project on stroke prediction dataset. You signed out in another tab or window. py ~/tmp/shape_f3. Contribute to Aftabbs/Stroke-Prediction-using-Machine-Learning development by creating an account on GitHub. For this purpose, I used the "healthcare-dataset-stroke-data" from Kaggle. Project Overview This project focuses on detecting brain strokes using machine learning techniques, specifically a Convolutional Neural Network (CNN) algorithm. The dataset consists of 11 clinical features which contribute to stroke occurence. 1. ; 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. To prepare the data for modelling, an Exploratory Data Analysis was conducted where it was detected that the dataset is very imbalance (95% no stroke, 5% stroke). This dataset was created by fedesoriano and it was last updated 9 months ago. 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. To get started In this project, we used logistic regression to discover the relationship between stroke and other input features. - KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset Heart disease prediction and Kidney disease prediction. This project predicts stroke occurrences using machine learning on a healthcare dataset. 3. Navigation Menu Toggle navigation Plan and track work Code Review. Write better code with AI Code review. py has the main function and contains all the required functions for the flask app. com Data Source: The healthcare-dataset-stroke-data. Sign in Product 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. Standard codes for the stroke data: synthea-stroke-dataset-codes. no risk) and regression (risk percentage prediction). GitHub community articles Skip to content. predict() method takes input from the request (once the 'compute' button from index. The goal here is to get the best accuracy on a larger dataset. Find and fix vulnerabilities Predicting whether a patient is likely to get stroke or not - terickk/stroke-prediction-dataset In this stroke prediction model we have implemented Logistic Regression, Random Forest & LightGBM. oun lvqpu blgtc lzwhqh aeqd hjij sag hlxgu ezvi cszqxigb eqiyiq isu cki xfbcz mcxxz