Brain stroke prediction dataset github python Stroke is a serious medical condition that occurs when the blood supply to part of the brain is interrupted or reduced, leading to brain damage and potential long-term disability or death. ipynb - check model predictions, specific patient output 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. Stroke is a condition that happens when the blood flow to the brain is impaired or diminished. Dataset: Stroke Prediction Dataset This repository contains code for a brain stroke prediction model built using machine learning techniques. Brain strokes are a leading cause of disability and death worldwide. About. The project demonstrates the potential of using logistic regression to assist in the stroke prediction and management of brain stroke using Python. 根据世界卫生组织(who)的数据，中风是全球第二大死亡原因，约占总死亡人数的11% 。这个数据集被用来根据输入的参数如性别、年龄、各种疾病和吸烟状况来预测病人是否可能得中风。 This repository contains a Stroke Prediction project implemented in Python using machine learning techniques. The dataset used in this article contains 5110 records of patients. ipynb as a Pandas DataFrame; Columns where the BMI value was "NaN" were dropped from the DataFrame Jun 24, 2022 · Once we have successfully installed and configured our environment, we can step right into our dataset. 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. Dec 10, 2022 · Brain Stroke is considered as the second most common cause of death. - GitHub - sa-diq/Stroke-Prediction: Prediction of stroke in patients using machine learning algorithms. This project aims to use machine learning to predict stroke risk, a leading cause of long-term disability and mortality worldwide. Welcome to the Fetal-BET (Fetal Brain Extraction Tool) repository! We've developed a powerful deep learning-based solution for automatic fetal brain extraction in MRI scans. Prediction of stroke in patients using machine learning algorithms. 2012-GIPSA. This project utilizes deep learning models like CNN, SVM, and VGG16 to accurately classify brain stroke images. txt │ README. Apr 27, 2023 · The objective is to create a user-friendly application to predict stroke risk by entering patient data. precision recall Motive: According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Project Structure. The code and open source algorithms I will be working with are written in Python, an extremely popular, well supported, and evolving data analysis language. py │ user_inp_output │ ├───. Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. Our solution is to: Step 1) create a classification model to predict whether an The dataset was skewed because there were only few records which had a positive value for stroke-target attribute In the gender attribute, there were 3 types - Male, Female and Other. The aim of this study is to check how well it can be predicted if patient will have barin stroke based on the available health data such as glucose level, age The improved model, which uses PCA instead of the genetic algorithm (GA) previously mentioned, achieved an accuracy of 97. Contribute to Rafe2001/Brain_Stroke_Prediction development by creating an account on GitHub. #The dataset aims to facilitate research and analysis to understand the factors associated with brain stroke occurrence, as well as develop prediction models to identify individuals who may be at a higher risk of stroke We segmented the Brain tumor using Brats dataset and as we know it is in 3D format we used the slicing method in which we slice the images in 2D form according to its 3 axis and then giving the model for training then combining waits to segment brain tumor. Manage code changes This repository contains a comprehensive analysis of stroke prediction using machine learning techniques. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Installation. Contributing. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. You switched accounts on another tab or window. The dataset includes 100k patient records. [ ] Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. json │ custom_dataset. Find and fix vulnerabilities You signed in with another tab or window. Find and fix vulnerabilities Codespaces. We use prin- Only BMI-Attribute had NULL values ; Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median. 5 %µµµµ 1 0 obj > endobj 2 0 obj > endobj 3 0 obj >/ExtGState >/Font >/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/Annots[ 13 0 R] /MediaBox[ 0 0 612 792 2019. 9. The Jupyter notebook notebook. - GitHub - 21AG1A05E4/Brain-Stroke-Prediction: The project demonstrates the potential of using logistic regression to assist in the stroke prediction and management of brain stroke using Python. This code is implementation for the - A. Language Used: • Python 3. healthcare-dataset-stroke-data. This repository holds code and resources for a machine learning project predicting probability of having brain stroke from medical data. ipynb │ ├───images │ Correlation Brain-Stroke-Prediction Python code for brain stroke detector. This dataset contains various attributes of individuals, including demographic information, medical history, and lifestyle factors, along with a binary target variable indicating whether the Write better code with AI Security. This project aims to predict strokes using factors like gender, age, hypertension, heart disease, marital status, occupation, residence, glucose level, BMI, and smoking. Anto, "Tumor detection and classification of MRI brain image using wavelet transform and SVM", 2017 International Conference on Signal Processing and Communic… Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. app. The random forest classifier provided the highest accuracy among the models for detecting brain stroke. Techniques: • Python-For Programming Logic • Application:-Used in application for GUI • Python :- Provides machine learning process The dataset used to predict stroke is a dataset from Kaggle. md │ user_input. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. Keywords: microwave imaging, machine learning algorithms, support vector machines, multilayer perceptrons, k-nearest neighbours, brain stroke. Stroke is a leading cause of death and disability worldwide. Developed using libraries of Python and Decision Tree Algorithm of Machine learning. Our project is entitled: "Prediction of brain tissues hemodynamics for stroke patients using computed tomography perfusion imaging and deep learning" Predicted stroke risk with 92% accuracy by applying logistic regression, random forests, and deep learning on health data. stroke prediction. Nov 21, 2024 · The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Researchers can use a variety of machine learning techniques to forecast the likelihood of a stroke occurring. 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. 背景描述. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. Table of Contents. It was trained on patient information including demographic, medical, and lifestyle factors. Initially an EDA has been done to understand the features and later 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. 7) Stroke is a disease that affects the arteries leading to and within the brain. its my final year project. License. csv │ Brain_Stroke_Prediction. py has the main function and contains all the required functions for the flask app. Early prediction of stroke risk plays a crucial role in preventive healthcare, enabling timely 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. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. 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}. The majority of brain strokes are caused by an unanticipated obstruction of the heart's and brain's regular operations. You signed out in another tab or window. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. The input variables are both numerical and categorical and will be explained below. The dataset is imbalanced and will resolve this by sampling (Oversampling), so in this dataset, we will focus on AUC-ROC and Recall metrics because we don't want to misclassify any stroke patient as a non-stroke patient 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. It's a medical emergency; therefore getting help as soon as possible is critical. 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction dataset. 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. This project focuses on building a Brain Stroke Prediction System using Machine Learning algorithms, Flask for backend API development, and React. Instant dev environments Target Versus Non-Target: 25 subjects testing Brain Invaders, a visual P300 Brain-Computer Interface using oddball paradigm. The competition provides a synthetic dataset that was generated from a deep learning model trained on the Stroke Prediction Dataset. x = df. This data is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and # Prompt the user for the dataset filename and load the data into a Pandas DataFrame Aug 5, 2022 · In this video,Im implemented some practical way of machine learning model development approaches with brain stroke prediction data👥For Collab, Sponsors & Pr Stroke is a brain attack. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. 27% uisng GA algorithm and it out perform paper result 96. This project aims to develop a predictive model to identify the likelihood of a brain stroke based on various health parameters. 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 api (fastapi, one prediction endpoint) api. For this we need to have potential solution to predict it So the process for the analysis was done and breakup of it is given below. The study uses a dataset with patient demographic and health features to explore the predictive capabilities of three algorithms: Artificial Neural Networks (ANN In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. This project predicts stroke disease using three ML algorithms - fmspecial/Stroke_Prediction Data Sources The dataset used for training and testing the Stroke Prediction System is sourced from the "healthcare-dataset-stroke-data. Stroke Prediction Using Machine Learning (Classification use case) Topics machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. Brain Stroke Dataset Attribute Information-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 Predicting whether a person suffers from stroke using Machine Learning. The value '0' indicates no stroke risk detected, whereas the value '1' indicates a possible risk of stroke. Mathew and P. slices in a CT scan. - Brain-Stroke-Prediction/README. Main Features: Stroke Risk Prediction: Utilizing supervised learning algorithms such as kNN, SVM, Random Forest, Decision Tree, and XGradient Boosting, this feature aims to develop predictive models to forecast the likelihood of an A stroke is a medical condition in which poor blood flow to the brain causes cell death [1]. 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. py - test of API, making call with image from dataset; notebooks (notebooks and analysis) model_predictions_analysis. It occurs when either blood flow is obstructed in a brain region (ischemic stroke) or sudden bleeding in the brain (hemorrhagic stroke). 3. Python is used for the frontend and MySQL for the backend. It is now possible to predict when a stroke will start by using ML approaches thanks to advancements in medical technology. This dataset is highly imbalanced as the possibility of '0' in the output column ('stroke') outweighs that of '1' in the same column. - Priyansh42/Stroke-Blood-Clot-Classification Brain Stroke Analysis Using Python and Power Bi. js for the frontend. - ansonnn07/stroke-prediction This repository contains code for a brain stroke prediction model that uses machine learning to analyze patient data and predict stroke risk. Dependencies Python (v3. Dataset The dataset used in this project contains information about various health parameters of individuals, including: The dataset used in the development of the method was the open-access Stroke Prediction dataset. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Kaggle is an AirBnB for Data Scientists. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome A stroke occurs when the brain gets damaged as a result of interruption of the blood supply. Predicting brain stroke by given features in dataset. ipynb │ Brain_Stroke_Prediction-checkpoint. This dataset has been used to predict stroke with 566 different model algorithms. A stroke prediction app using Streamlit is a user-friendly tool designed to assess an individual's risk of experiencing a stroke. model --lrsteps 200 250 --epochs 300 --outbasepath ~/tmp/shape --channelscae 1 16 24 32 100 200 1 --validsetsize 0. K-nearest neighbor and random forest algorithm are used in the dataset. Using the publicly accessible stroke prediction dataset, the study measured four commonly used machine learning methods for predicting brain stroke recurrence, which are as follows: (i) Random forest (ii) Decision tree (iii) Logistic regression Aug 28, 2021 · Image from Canva Basic Tooling. Each patient has 12 columns each referring to a concrete attribute. %PDF-1. ; 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 WHO identifies stroke as the 2nd leading global cause of death (11%). By inputting relevant health data such as age, blood pressure, cholesterol levels, and lifestyle factors, the app utilizes predictive algorithms to calculate the user's likelihood of having a stroke. Contribute to Yogha961/Brain-stroke-prediction-using-machine-learning-techniques development by creating an account on GitHub. In this project, various classification algorithm will be evaluated to find the best model for the dataset. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. It gives users a quick understanding of the dataset's structure. We intend to create a progarm that can help people monitor their risks of getting a stroke. Segmenting stroke lesions accurately is a challeng-ing task, given that conventional manual techniques are time-consuming and prone to errors. 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. Jul 1, 2023 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. │ brain_stroke. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). Project description: According to WHO, stroke is the second leading cause of dealth and major cause of disability worldwide. This model differentiates between the two major acute ischemic stroke (AIS) etiology subtypes: cardiac and large artery atherosclerosis enabling healthcare providers to better identify the origins of blood clots in deadly strokes. Feature Selection: The web app allows users to select and analyze specific features from the dataset. It utilizes machine learning techniques, including SMOTE for handling imbalanced data, stacking classifiers for improved accuracy, and a GUI-based prediction tool using Tkinter. 2D CNNs are commonly used to process both grayscale (1 channel) and RGB images (3 channels), while a 3D CNN represents the 3D equivalent since it takes as input a 3D volume or a sequence of 2D frames, e. Contribute to iamadi1709/Brain-Stroke-Detection-from-CT-Scans-via-3D-Convolutional-Neural-Network development by creating an account on GitHub. Timely prediction and prevention are key to reducing its burden. md at main · Kiroves/Brain-Stroke-Prediction Contribute to Vikram3003/Stroke-Analysis-and-Prediction-Python development by creating an account on GitHub. Stacking. A dataset from Kaggle is used, and data preprocessing is applied to balance the dataset. Following preprocessing and model tuning, it achieves high accuracy in detecting stro Keywords: brain stroke, deep learning, machine learning, classification, segmentation, object detection. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. The risk of stroke is affected by a wide range of factors, including age, gender, hypertension, heart disease, obesity, and smoking. Our primary objective is to develop a robust predictive model for identifying potential brain stroke occurrences, a Automate any workflow Packages After a stroke, some brain tissues may still be salvageable but we have to move fast. machine-learning random-forest svm jupyter-notebook logistic-regression lda knn baysian stroke-prediction Write better code with AI Security Brain strokes are a leading cause of disability and death worldwide. This project aims to predict the likelihood of stroke occurrence based on medical and demographic data. zip │ New Text Document. Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Brain stroke is a cardiovascular disease that occurs when the blood flow becomes abnormal in a region of the head. html is pressed) and converts it into an array. The effects can lead to brain damage with loss of vision, speech, paralysis and, in many cases, death. 60 % accuracy. Contribute to AruneshkannanK/brain-stroke-prediction- development by creating an account on GitHub. If blood flow was stopped for longer than a few seconds and the brain cannot get blood and oxygen, brain cells can die, and the abilities controlled by that area of the brain are lost. Reload to refresh your session. Project Overview This project focuses on detecting brain strokes using machine learning techniques, specifically a Convolutional Neural Network (CNN) algorithm. Key features of the dataset include attributes related to various aspects of an individual's health, demographics This university project aims to predict brain stroke occurrences using a publicly available dataset. Introduction Machine learning algorithms and projects . Dataset. Optimized dataset, applied feature engineering, and implemented various algorithms. In addition, the majority of studies are in stroke diagnosis whereas the majority of studies are in stroke treatment, indicating a research gap that needs to be filled. using visualization libraries, ploted various plots like pie chart, count plot, curves This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Alleviate healthcare costs associated with long-term stroke care. There was only 1 record of the type "other", Hence it was converted to the majority type – decrease the dimension Jun 12, 2024 · This code provides the Matlab implementation that detects the brain tumor region and also classify the tumor as benign and malignant. 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. publication , code . Resources Brain Stroke Prediction - Machine Learning Model. This enhancement shows the effectiveness of PCA in optimizing the feature selection process, leading to significantly better performance compared to the initial accuracy of 61. In ten investigations for stroke issues, Support Vector Machine (SVM) was found to be the best models. Contribute to akpythonyt/ML-projects development by creating an account on GitHub. GitHub repository for stroke prediction project. 8. The high mortality and long-term care requirements impose a significant burden on healthcare systems and families. to make predictions of stroke cases based on simple health Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. Achieved high recall for stroke cases. Dataset id: BI. In the code, we have created the instance of the Flask() and loaded the model. 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. 3. The model uses machine learning algorithms to analyze patient data and predict the risk of stroke, which can help in early diagnosis and preventive care. The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. This code performs data preprocessing, applies SMOTE for handling class imbalance, trains a Random Forest Classifier on a brain stroke dataset, and evaluates the model using accuracy, classification report, and confusion matrix. Manage code changes Jan 20, 2023 · Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques January 2023 European Journal of Electrical Engineering and Computer Science 7(1):23-30 Dec 10, 2022 · A stroke is an interruption of the blood supply to any part of the brain. Our objective is twofold: to replicate the methodologies and findings of the research paper "Stroke Risk Prediction with Machine Learning Techniques" and to implement an alternative version using best practices in machine learning and data analysis. The model is trained on a dataset of patient information and various health metrics to predict the likelihood of an individual experiencing a stroke. EEG. For stroke survivors, while escaping death, they may still live with other complications (from the loss of blood to the brain) such as memory loss, speech impairment, eating disabilities, and/or loss of normal bodily functions . The Dataset Stroke Prediction is taken in Kaggle. The output attribute is a Dec 11, 2022 · Stroke is a condition that happens when the blood flow to the brain is impaired or diminished. model. Stroke is considered as medical urgent situation and can cause long-term neurological damage, complications and often death. Here, we try to improve the diagnostic/treatment process. Brain stroke, also known as a cerebrovascular accident, is a critical medical condition that requires immediate attention. According to the WHO, stroke is the 2nd leading cause of death worldwide. In this paper, we attempt to bridge this gap by providing a systematic analysis of the various patient records for the purpose of stroke prediction. Python Write better code with AI Code review. This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. - mmaghanem/ML_Stroke_Prediction 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. Globally, 3% of the population are affected by subarachnoid hemorrhage… Contribute to Chando0185/Brain_Stroke_Prediction development by creating an account on GitHub. 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 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. Contribute to itisaritra/brain_stroke_prediction development by creating an account on GitHub. ipynb. py - simple API for making predictions on brain images, outputs segmentation mask (without thresholding) api_test. It standardizes the brain stroke dataset and evaluates the performance of different classifiers. It takes different values such as Glucose, Age, Gender, BMI etc values as input and predict whether the person has risk of stroke or not. This is basically a classification problem. 60%. Usage. WHO identifies stroke as the 2nd leading global cause of death (11%). Only 248 rows have the value '1 Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. 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. The main objective is to predict strokes accurately while exploring the strengths and limitations of each model. Contribute to aryan7iitj/Brain_Stroke_Prediction development by creating an account on GitHub. The dataset used in the development of the method was the open-access Stroke Prediction dataset. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. - skp163/Stroke_Prediction The output column stroke has the values either ‘1’ or ‘0’. Globally, 3% of the population are affected by subarachnoid hemorrhage… Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. predict() method takes input from the request (once the 'compute' button from index. 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. We need to store our output or outcome of prediction from our dataset on a separate set, this is also called as our target-set, and we also have To predict brain stroke from patient's records such as age, bmi score, heart problem, hypertension and smoking practice. Seeking medical help right away can help prevent brain damage and other complications. We used UNET model for our segmentation. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. This project investigates the potential relationship between work status, hypertension, glucose levels, and the incidence of brain strokes. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model The most common disease identified in the medical field is stroke, which is on the rise year after year. 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. [2]. Similar to this, CT pictures are a common dataset in stroke. Abstract—Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. csv was read into Data Extraction. Feature-Target Set. 16-electrodes, wet. Introduction to the dataset. this project contains code for brain stroke prediction using public dataset, includes EDA, model training, and deploying using streamlit - samata18/brain-stroke-prediction . Problem Statement : The problem statement for the analysis on the data was whether the person will have brain stroke or not. Our method, built on a vast dataset of 72,000 images, excels in extracting fetal brain structures from diverse MRI sequences and scanning conditions. Nov 19, 2024 · Welcome to the ultimate guide on Brain Stroke Prediction Using Python & Machine Learning ! In this video, we'll walk you through the entire process of making Feb 11, 2022 · Dataset After pre-processing. project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. json │ user_input. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. It is one of the main causes of death and disability. This dataset was created by fedesoriano and it was last updated 9 months ago. Stroke is a disease that affects the arteries leading to and within the brain. Brain stroke, also known as a cerebrovascular accident, is a critical medical condition that occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissue from receiving oxygen and The dataset for this project originates from the Kaggle Playground Series, Season 3, Episode 2. Early prediction of stroke risk can help in taking preventive measures. For learning the shape space on the manual segmentations run the following command: train_shape_reconstruction. Introduction. ipynb contains the model experiments. Utilizing a dataset from Kaggle, we aim to identify significant factors that contribute to the likelihood of brain stroke occurrence. py │ images. py ~/tmp/shape_f3. drop(['stroke'], axis=1) y = df['stroke'] 12. Process Steps: 1. g. 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 Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. Algorithms are compared to select the best for stroke prediction. Leveraged skills in data preprocessing, balancing with SMOTE, and hyperparameter optimization using KNN and Optuna for model tuning. This project studies the use of machine learning techniques to predict the long-term outcomes of stroke victims. The followed approach is based on the usage of a 3D Convolutional Neural Network (CNN) in place of a standard 2D one. By developing a predictive model, we aim to: Reduce the incidence of stroke through early intervention. csv" file. . Software: • Anaconda, Jupyter Notebook, PyCharm. Write better code with AI Code review. Our task is to predict whether a patient will suffer a stroke or not given the medical data of that patient. ipynb │ config. 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. Globally, 3% of the Brain stroke prediction using machine learning machine-learning logistic-regression beginner-friendly decision-tree-classifier kaggle-dataset random-forest-classifier knn-classifier commented introduction-to-machine-learning xgboost-classifier brain-stroke brain-stroke-prediction Aug 25, 2022 · More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. zip │ models. It is also referred to as Brain Circulatory Disorder. 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 aims to predict the likelihood of a person having a brain stroke using machine learning techniques. 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. Prediction of brain stroke based on imbalanced dataset in Stroke is a disease that affects the arteries leading to and within the brain. - Akshit1406/Brain-Stroke-Prediction Description: This GitHub repository offers a comprehensive solution for predicting the likelihood of a brain stroke. 1. Learn more Activate the above environment under section Setup. Stroke Prediction Using Python. ipynb_checkpoints │ Brain_Stroke_Prediction (1)-checkpoint.