Best brain stroke prediction dataset github We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. First, in the pre-processing stage, they used two dimensional (2D) discrete wavelet transform (DWT) for brain images. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. g. 3. 5% of them are related to stroke patients and the remaining 98. The imbalanced classes created an uphill battle for the models. Utilizing a dataset from Kaggle, we aim to identify significant factors that contribute to the likelihood of brain stroke occurrence. Datasets and resources listed here should all be openly-accessible for research purposes, requiring, at most, registration for access. Dataset id: BI. Project Overview This project focuses on detecting brain strokes using machine learning techniques, specifically a Convolutional Neural Network (CNN) algorithm. You signed in with another tab or window. Brain stroke, also known as a cerebrovascular accident, is a critical medical condition that requires immediate attention. Among the records, 1. 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. Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. Globally, 3% of the Stroke is a leading cause of death and disability worldwide. 52 52 avg / total 0. Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. 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. This is a list of openly available electrophysiological data, including EEG, MEG, ECoG/iEEG, and LFP data. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. 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. - GitHub - Assasi At the conclusion of segment 1 of this project we have tried several different machine learning models with this dataset (RandomForestClassifier, BalancedRandomForestClassifier, LogisticRegression, and Neural Network). 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 Feb 7, 2024 · Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. 77 0. Introduction. In addition to the features, we also show results for stroke prediction when principal components are used as the input. 3. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy. Aug 29, 2024 · The stroke disease prediction system. The area of the brain which is deprived of blood is thus damaged, resulting in signs and symptoms of stroke. This underscores the need for early detection and prevention The most common disease identified in the medical field is stroke, which is on the rise year after year. stroke prediction. Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. Aug 25, 2022 · More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. html is pressed) and converts it into an array. WHO identifies stroke as the 2nd leading global cause of death (11%). The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. Feature Selection: The web app allows users to select and analyze specific features from the dataset. 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 This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. 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 If not available on GitHub, the notebook can be accessed on nbviewer, or alternatively on Kaggle. 5% of them are related to non-stroke patients. 1906) compared to the XGBoost model (0. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. We don't want any patient who is suffering from a stroke to be categorized as having a non-stroke and so not receive the necessary medical care Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. This research investigates the application of robust machine learning (ML) algorithms, including Saritha et al. How can this help patients in stroke prevention? This repository contains code for a brain stroke prediction model that uses machine learning to analyze patient data and predict stroke risk. The main objective is to predict strokes accurately while exploring the strengths and limitations of each model. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. " According to Ministry of Health Singapore, 1 in 4 people will suffer from a strole in their lifetime. This is a serious health issue and the patient having this often requires immediate and intensive treatment. using visualization libraries, ploted various plots like pie chart, count plot, curves Dealing with Class Imbalance. 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. GitHub repository for stroke prediction project. AUC-PR measures the area under the precision-recall curve and provides an aggregate measure of model performance for imbalanced datasets. isnull(). Extra Trees Classifier Model performs the best with 100% Recall and 99. Acknowledgements (Confidential Source) - Use only for educational purposes If you use this dataset in your research, please credit the author. Saved searches Use saved searches to filter your results more quickly <class 'pandas. 76 0. Learn more Description: This GitHub repository offers a comprehensive solution for predicting the likelihood of a brain stroke. 98 0. This is basically a classification problem. The study uses a dataset with patient demographic and health features to explore the predictive capabilities of three algorithms: Artificial Neural Networks (ANN The Jupyter notebook notebook. 67 0. Keywords: microwave imaging, machine learning algorithms, support vector machines, multilayer perceptrons, k-nearest neighbours, brain stroke. ; Data Visualization and Exploratory Data Analysis: The code contains visualizations for various aspects of the data, such as age distribution, BMI, glucose levels, and categorical feature distributions. predict() method takes input from the request (once the 'compute' button from index. AUC-PR: The Neural Network model has a slightly higher AUC-PR score (0. Stroke prediction is a critical area of research in healthcare, as strokes are one of the leading global causes of mortality (WHO: Top 10 Causes of Death). 1. Dataset The dataset used in this project contains information about various health parameters of individuals, including: 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. 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 rupture or blockage prevents blood and oxygen from reaching the brain’s tissues. The d 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. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. 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. 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. - mmaghanem/ML_Stroke_Prediction To predict brain stroke from patient's records such as age, bmi score, heart problem, hypertension and smoking practice. 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 Contribute to Cvssvay/Brain_Stroke_Prediction_Analysis development by creating an account on GitHub. This project predicts stroke disease using three ML algorithms - fmspecial/Stroke_Prediction A stroke is a medical condition in which poor blood flow to the brain causes cell death [1]. Achieved high recall for stroke cases. Check for Missing values # lets check for null values df. There was only 1 record of the type "other", Hence it was converted to the majority type – decrease the dimension This university project aims to predict brain stroke occurrences using a publicly available dataset. The structure of the stroke disease prediction system is shown in Fig. 2 million new strokes each year [1]. Timely prediction and prevention are key to reducing its burden. This suggested system has the following six phases: (1) Importing a dataset of Nov 8, 2024 · One of the major subclasses of CVDs is stroke, a medical condition in which poor blood flow to the brain causes cell death and makes the brain stop functioning properly. 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. There was only 1 record of the type "other", Hence it was converted to the majority type – decrease the dimension 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. The stroke prediction module for the elderly using deep learning-based real-time EEG data proposed in this paper consists of two units, as illustrated in Figure 4. 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. csv. 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 Only BMI-Attribute had NULL values ; Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median. publication , code . Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. ipynb_checkpoints │ Brain_Stroke_Prediction (1)-checkpoint. 71 0. ipynb contains the model experiments. 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. Stroke is a disease that affects the arteries leading to and within the brain. There are more female than male in the data set. The objective is to accurately classify CT scans as exhibiting signs of a stroke or not, achieving high accuracy in stroke detection based on radiological imaging. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. It occurs when either blood flow is obstructed in a brain region (ischemic stroke) or sudden bleeding in the brain (hemorrhagic stroke). This RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. 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. 16-electrodes, wet. In the code, we have created the instance of the Flask() and loaded the model. 95 0. The value of the output column stroke is either 1 or 0. - Brain-Stroke-Prediction/README. 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. Dataset: Stroke Prediction Dataset This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. We use prin- This repository contains a comprehensive analysis of stroke prediction using machine learning techniques. According to the WHO, stroke is the 2nd leading cause of death worldwide. to make predictions of stroke cases based on simple health In this project, various classification algorithm will be evaluated to find the best model for the dataset. Resources To predict what factors influence a person’s stroke, I will utilize the stroke variable as the dependent variable. We intend to create a progarm that can help people monitor their risks of getting a stroke. The output column stroke has the values either ‘1’ or ‘0’. The main script stroke_prediction. 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. For learning the shape space on the manual segmentations run the following command: train_shape_reconstruction. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). Interestingly two of the stronger correlating factors to stroke, average glucose level and hypertension, were non-factors for prediction in the best model. The Beneficiaries. Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for This project aims to predict the likelihood of a person having a brain stroke using machine learning techniques. Brain stroke is a cardiovascular disease that occurs when the blood flow becomes abnormal in a region of the head. md │ user_input. It includes preprocessed datasets, exploratory data analysis, feature engineering, and various predictive models. 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. 66 0. 7) Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction 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. Our work also determines the importance of the characteristics available and determined by the dataset. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. 2. The objective is to predict brain stroke from patient's records such as age, bmi score, heart problem, hypertension and smoking practice. The value '0' indicates no stroke risk detected, whereas the value '1' indicates a possible risk of stroke. Early prediction of stroke risk plays a crucial role in preventive healthcare, enabling timely 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. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. My trained Random Forest model on the SMOTE-NC stroke data Oct 1, 2023 · A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. The leading causes of death from stroke globally will rise to 6. This project studies the use of machine learning techniques to predict the long-term outcomes of stroke victims. This report aims to Target Versus Non-Target: 25 subjects testing Brain Invaders, a visual P300 Brain-Computer Interface using oddball paradigm. S. txt │ README. json │ user_input. 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. - sakshitodi/Stroke-Prediction Predicted stroke risk with 92% accuracy by applying logistic regression, random forests, and deep learning on health data. Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. Only 248 rows have the value '1 Stroke Prediction Using Machine Learning (Classification use case) Topics machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction Sep 15, 2022 · Authors Visualization 3. By developing a predictive model, we aim to: Reduce the incidence of stroke through early intervention. Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Dependencies Python (v3. 100% accuracy is reached in this notebook. K-nearest neighbor and random forest algorithm are used in the dataset. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. Our contribution can help predict early signs and prevention of this deadly disease - Brain_Stroke_Prediction_Using This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. json │ custom_dataset. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. Easy Ensemble AdaBoost Classifier Balanced Accuracy Score: 0. Contribute to AshutoshBiswal26/Brain-Stroke-Prediction 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. We get the conclusion that age, hypertension and work type self-employed would affect the possibility of getting stroke. The given Dataset is used to predict whether a patient is likely to get a stroke based on the input parameters like gender, age, various diseases, and smoking status. , where stroke is the fifth-leading cause of death. Dataset. 50 1176 1 0. Reload to refresh your session. 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. Our solution is to: Step 1) create a classification model to predict whether an 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. 09 0. The input variables are both numerical and categorical and will be explained below. For example, intracranial hemorrhages account for approximately 10% of strokes in the U. csv │ Brain_Stroke_Prediction. Focused on predicting the likelihood of brain strokes using machine learning. Optimized dataset, applied feature engineering, and implemented various algorithms. Jan 1, 2024 · To this day, acute ischemic stroke (AIS) is one of the leading causes of morbidity and disability worldwide with over 12. Stroke is a brain attack. The dataset used in the development of the method was the open-access Stroke Prediction dataset. ipynb │ Brain_Stroke_Prediction-checkpoint. Deep learning (DL) contributes to stroke treatment by detecting infarcts or hemorrhages, segmenting images, identifying large vessel occlusions, early detection, and providing Nov 1, 2022 · Here we present results for stroke prediction when all the features are used and when only 4 features (A, H D, A G and H T) are used. Be sure to check the license and/or usage agreements for Contribute to shakthi-20/ML-based-Brain-stroke-prediction development by creating an account on GitHub. By doing so, it also urges medical users to strengthen the motivation of health management and induce changes in their health behaviors. 79 0. The output attribute is a 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. EEG. Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. There were 5110 rows and 12 columns in this dataset. sum() OUTPUT: id 0 gender 0 age 0 hypertension 0 heart_disease 0 ever_married 0 work_type 0 Residence Contribute to Chando0185/Brain_Stroke_Prediction development by creating an account on GitHub. It is estimated that the global cost of stroke is exceeding US$ 721 billion and it remains the second-leading cause of death and the third-leading cause of death and disability combined [1]. Stroke Prediction Module. You switched accounts on another tab or window. The dataset includes 100k patient records. Analysis of the Stroke Prediction Dataset provided on Kaggle. Stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. Result and ROI. Manage code changes The followed approach is based on the usage of a 3D Convolutional Neural Network (CNN) in place of a standard 2D one. - GitHub - sa-diq/Stroke-Prediction: Prediction of stroke in patients using machine learning algorithms. py │ images. The best-performing model is deployed in a web-based application, with future developments including real-time data integration. - ajspurr/stroke_prediction In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. 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. Column Name Data Type Description; id: Integer: Unique identifier: gender: Object "Male", "Female", "Other" age: Float: Age of patient: hypertension: Integer: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension Brain-Stroke-Prediction. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. This dataset is highly imbalanced as the possibility of '0' in the output column ('stroke') outweighs that of '1' in the same column. 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. This dataset was created by fedesoriano and it was last updated 9 months ago. Dec 11, 2022 · This project hence helps to predict the stroke risk using prediction model and provide personalized warning and the lifestyle correction message. Kaggle is an AirBnB for Data Scientists. Stacking. 1545). Aug 28, 2021 · We can see on this data set the Random Forest Classifier performs superior to Regression, SVM, and K-Nearest Neighbors. py ~/tmp/shape_f3. It is one of the main causes of death and disability. We aim to identify the factors that con 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. model. integrated wavelet entropy-based spider web plots and probabilistic neural networks to classify brain MRI, which were normal brain, stroke, degenerative disease, infectious disease, and brain tumor in their study. Jun 24, 2022 · For the purposes of this article, we will proceed with the data provided in the df variable. py has the main function and contains all the required functions for the flask app. 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. Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. 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. Leveraged skills in data preprocessing, balancing with SMOTE, and hyperparameter optimization using KNN and Optuna for model tuning. zip │ models. These features are selected based on our earlier discussions. 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. 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 Models performed above average. Early prediction of stroke risk can help in taking preventive measures. Contribute to orkunaran/Stroke-Prediction development by creating an account on GitHub. model --lrsteps 200 250 --epochs 300 --outbasepath ~/tmp/shape --channelscae 1 16 24 32 100 200 1 --validsetsize 0. The provided text contains a series of code snippets and outputs related to the analysis of a dataset for predicting the risk of stroke. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. The goal is to optimize classification performance while addressing challenges like imbalanced datasets and high false-positive rates in medical predictions. . The high mortality and long-term care requirements impose a significant burden on healthcare systems and families. ; 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 . machine-learning random-forest svm jupyter-notebook logistic-regression lda knn baysian stroke-prediction Aim of this project. Prediction of brain stroke based on imbalanced dataset in A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. The stroke prediction dataset was used to perform the study. Sep 22, 2023 · About Data Analysis Report. Two datasets consisting of brain CT images were utilized for training and testing the CNN models. frame. │ brain_stroke. Without oxygen, brain cells and tissue become damaged and begin to die within minutes. Globally, 3% of the population are affected by subarachnoid hemorrhage… The Dataset Stroke Prediction is taken in Kaggle. 16 0. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. py │ user_inp_output │ ├───. 2012-GIPSA. Doctors could make the best use of this approach to decide and act upon accordingly for patients with high risk would require different treatment and medication since the time of admission. This project aims to predict strokes using factors like gender, age, hypertension, heart disease, marital status, occupation, residence, glucose level, BMI, and smoking. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. This project implements various neural network models to predict strokes using the Stroke Prediction Dataset from Kaggle. A stroke occurs when a blood vessel in the brain ruptures and bleeds, or when there’s a blockage in the blood supply to the brain. Using a machine learning based approach to predict hemorrhagic stroke severity in susceptible patients. ipynb │ config. app. 7162480376766092 Predicted No Stroke Predicted Stroke Actual No Stroke 780 396 Actual Stroke 12 40 pre rec spe f1 geo iba sup 0 0. 51 1228 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. core. 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. Initially an EDA has been done to understand the features and later The dataset used to predict stroke is a dataset from Kaggle. The project aims to assist in early detection by providing accurate predictions, potentially reducing risks and improving patient outcomes. A stroke occurs when there is a disruption of blood flow to the brain. Exploratory Data Analysis. 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. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. For the offline processing unit, the EEG data are extracted from a database storing the data on various biological signals such as EEG, ECG, and EMG A Machine Learning Project predicting the chances of having a brain stroke based on few of human health parameters - Shanhavi23/Brain-Stroke-Prediction Contribute to jageshkarS/stroke-prediction development by creating an account on GitHub. Brain strokes are a leading cause of disability and death worldwide. It was trained on patient information including demographic, medical, and lifestyle factors. Researchers can use a variety of machine learning techniques to forecast the likelihood of a stroke occurring. zip │ New Text Document. Nov 21, 2023 · 12) stroke: 1 if the patient had a stroke or 0 if not *Note: "Unknown" in smoking_status means that the information is unavailable for this patient. slices in a CT scan. 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 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 Prediction of stroke in patients using machine learning algorithms. The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. After applying Exploratory Data Analysis and Feature Engineering, the stroke prediction is done by using ML algorithms including Ensembling methods. 8% AUC score; The model does not misclassify any stroke patient as a non-stroke patient, which is fascinating. Machine Learning techniques including Random Forest, KNN , XGBoost , Catboost and Naive Bayes have been used for prediction. py contains the following functionalities: Data preprocessing Model training Model evaluation To run the script, simply execute the cells in the notebook. 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. project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. A stroke is a medical condition in which poor blood flow to the brain causes cell death. Stroke is a medical condition that occurs when blood vessels in the brain are ruptured or blocked, resulting in brain damage. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. 9. md at main · Kiroves/Brain-Stroke-Prediction Contribute to Tomal991/Brain-Stroke-Prediction-with-Oversampling-in-Machine-Learning development by creating an account on GitHub. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing Jan 20, 2023 · The brain is the human body's primary upper organ. The code consists of the following sections: Data Loading and Preprocessing: The data is loaded from the CSV file and preprocessed, including handling missing values. Write better code with AI Code review. 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. We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction dataset. js for the frontend. Activate the above environment under section Setup. The effects can lead to brain damage with loss of vision, speech, paralysis and, in many cases, death. It gives users a quick understanding of the dataset's structure. This dataset has been used to predict stroke with 566 different model algorithms. These datasets typically include demographic information, medical histories, lifestyle factors and biomarker data from individuals, allowing ML algorithms to uncover complex patterns and interactions among risk factors. ipynb │ ├───images │ Correlation Stroke is a disease that affects the arteries leading to and within the brain. Most were overfit. This project focuses on building a Brain Stroke Prediction System using Machine Learning algorithms, Flask for backend API development, and React. The analysis includes data preprocessing, exploration, and the application of various machine learning models to predict the risk of stroke. 1 below. 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 In this project, we used logistic regression to discover the relationship between stroke and other input features. Resources This project investigates the potential relationship between work status, hypertension, glucose levels, and the incidence of brain strokes. Intracranial Hemorrhage is a brain disease that causes bleeding inside the cranium. Alleviate healthcare costs associated with long-term stroke care. 2. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. Globally, 3% of the population are affected by subarachnoid hemorrhage… This project aims to use machine learning to predict stroke risk, a leading cause of long-term disability and mortality worldwide. cdq dvur uwskk afm zrqijp umum hyv ybwzflmc mwcnm ijfqfy ixend vycjbw objhckith dvlbea udszae