Brain stroke prediction dataset. Lesion location and lesion overlap with extant brain .

Brain stroke prediction dataset Nov 21, 2024 · The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. The output attribute is a has been carried out on the prediction of heart stroke but very few works show the risk of a brain stroke. Given the rising prevalence of strokes, it is critical to understand the many factors that contribute to these occurrences. Our ML model uses a dataset for survival prediction to determine a patient's likelihood of suffering a stroke based on inputs including gender, age, various illnesses, and smoking status. Learn more. 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Nov 1, 2022 · The dataset is highly unbalanced with respect to the occurrence of stroke events; most of the records in the EHR dataset belong to cases that have not suffered from stroke. Machine learning for brain stroke: A review. May 20, 2024 · The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the data used in this study 38,39. Currently, there is no effective method to predict a stroke using warning signs and hereditary factors. 2 million new strokes each year [1]. J. g. May 19, 2024 · PDF | On May 19, 2024, Viswapriya Subramaniyam Elangovan and others published Analysing an imbalanced stroke prediction dataset using machine learning techniques | Find, read and cite all the Nov 9, 2024 · The prediction of brain stroke is based on the Kaggle dataset accessed in September 2024. Apr 21, 2023 · 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 Jan 1, 2024 · To this day, acute ischemic stroke (AIS) is one of the leading causes of morbidity and disability worldwide with over 12. 3. Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. 28% for brain stroke prediction on the selected dataset. A strong prediction framework must be developed to identify a person's risk for stroke. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. Dec 28, 2024 · This retrospective observational study aimed to analyze stroke prediction in patients. Nov 26, 2021 · The dataset used in the development of the method was the open-access Stroke Prediction dataset. Dec 7, 2024 · Libraries Used: Pandas, Scitkitlearn, Keras, Tensorflow, MatPlotLib, Seaborn, and NumPy DataSet Description: The Kaggle stroke prediction dataset contains over 5 thousand samples with 11 total features (3 continuous) including age, BMI, average glucose level, and more. Fig. 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. Dataset: Stroke Prediction Dataset Nov 26, 2021 · 2. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. 55% using the RF classifier for the stroke prediction dataset. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. 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. 2 and Jun 9, 2021 · This research article aims apply Data Analytics and use Machine Learning to create a model capable of predicting Stroke outcome based on an unbalanced dataset containing information about 5110 Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. Feb 20, 2018 · Recently, efforts for creating large-scale stroke neuroimaging datasets across all time points since stroke onset have emerged and offer a promising approach to achieve a better understanding of Despite advancements, stroke prediction faces challenges, including data imbalance, limited real-time brain imaging models, and reliance on structured datasets such as those from Kaggle[4]. The dataset contains information from a sample of individuals, including both stroke and non-stroke cases. Stroke-GFCN: segmentation of Ischemic brain lesions. Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. The publisher of the dataset has ensured that the ethical requirements related to this data are ensured to the highest standards. This research aims to use neural network (NN) and machine learning (ML) techniques to assess the probability of a stroke in the brain occurring Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. 6 days ago · This study proposes an accurate predictive model for identifying stroke risk factors. The Brain stroke prediction model is trained on a public dataset provided by the Kaggle . This dataset has been used to predict stroke with 566 different model algorithms. Sep 1, 2024 · B. Lesion location and lesion overlap with extant brain Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. , measures of brain structure) of long-term stroke recovery following rehabilitation. Utilizing a dataset from Kaggle, we aim to identify significant factors that contribute to the likelihood of brain stroke occurrence. Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. The value of the output column stroke is either 1 or 0. 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 May 12, 2021 · We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction Jan 20, 2023 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. RELEVANT WORK The majority of strokes are seen as ischemic stroke and hemorrhagic stroke and are shown in Fig. 23050. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. 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}. Jul 1, 2023 · The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. 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]. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% stroke To assemble a varied dataset of brain imaging scans withdiagnosis. There were 5110 rows and 12 columns in this dataset. Apr 27, 2023 · The proposed system uses an ensemble of machine learning algorithms like KNN, decision tree, random forest, SVM and CatBoost for classification. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. biomarkers associated with stroke prediction. The effectiveness of several machine learning (ML of all fatalities. Dataset. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. 1. Jan 14, 2025 · 3. May 15, 2024 · Brain stroke detection using deep convolutional neural network (CNN) models such as VGG16, ResNet50, and DenseNet121 is successfully accomplished by presenting a framework and fundamental principles. Dec 31, 2024 · Although cardiac stroke prediction has received a lot of attention, brain stroke risk has received comparatively little attention. A. , ischemic or hemorrhagic stroke [1]. Dataset The dataset used in this project contains information about various health parameters of individuals, including: Oct 4, 2024 · Stroke prediction dataset, available online: (2022). Nov 18, 2024 · Among all the datasets, missing values has been spotted in the brain stroke dataset only. The severity for a stroke can be reduced by detecting it early on. Implementation of the study: "The Use of Deep Learning to Predict Stroke Patient Mortality" by Cheon et al. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. . 1 Brain stroke prediction dataset. Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. ipynb as a Pandas DataFrame; Columns where the BMI value was "NaN" were dropped from the DataFrame application of ML-based methods in brain stroke. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. This study presents a new machine learning method for detecting brain strokes using patient information. Only BMI-Attribute had NULL values ; Plotted BMI's value distribution - looked skewed - therefore imputed the missing values using the median. Implementation of DeiT (Data-Efficient Image Transformer) for accurate and efficient brain stroke prediction using deep learning techniques. Deep learning (DL) contributes to stroke treatment by detecting infarcts or hemorrhages, segmenting images, identifying large vessel occlusions, early detection, and providing May 24, 2024 · The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the Câmara J. Machine learning techniques show good accuracy in predicting the likelihood of a stroke from related factors. INTRODUCTION Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health Keywords: brain stroke, deep learning, machine learning, classification, segmentation, object detection. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. As a leading cause of death, strokes have been regarded as a dangerously impactful condition with little to no predictability. This project investigates the potential relationship between work status, hypertension, glucose levels, and the incidence of brain strokes. [6] labeled The title is "Automated Detection and Classification of Ischemic Stroke using Convolutional Neural Networks" Writers: characteristics,Thompson L. Accessed: 2022-07-25. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. Aug 20, 2024 · This study focuses on the intricate connection between general health, blood pressure, and the occurrence of brain strokes through machine learning algorithms. We developed a quantitative method to predict strokes before happening. Jul 4, 2024 · Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. csv at master · fmspecial/Stroke_Prediction most of the datasets, our dataset focuses on attributes that would have a major risk factors of a Brain Stroke. In this research work, with the aid of machine learning (ML 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. Nov 19, 2023 · By employing extended datasets of images to train the model, the accuracy of the model for brain stroke prediction can be further improved. Kaggle is an AirBnB for Data Scientists. Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. 13140/RG. The dataset included 401 cases of healthy individuals and 262 cases of stroke patients admitted in hospital This project predicts stroke disease using three ML algorithms - Stroke_Prediction/Stroke_dataset. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. Contribute to Cvssvay/Brain_Stroke_Prediction_Analysis development by creating an account on GitHub. Mar 4, 2022 · Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate the prediction process for the early detection of symptoms related to stroke so . From 2007 to 2019, there were roughly 18 studies associated with stroke diagnosis in the subject of stroke prediction using machine learning in the ScienceDirect database [4]. The experiments used five different classifiers, NB, SVM, RF, Adaboost, and XGBoost, and three feature selection methods for brain stroke prediction, MI, PC, and FI. The database is biased toward the negative class. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. 3. 1,2 Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke May 1, 2024 · This study proposed a hybrid system for brain stroke prediction (HSBSP) using data from the Stroke Prediction Dataset. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. Brain stroke prediction dataset. By leveraging various ML algorithms, it identifies stroke-related patterns and demonstrates improved performance on a dataset of 5110 patient records. Ischemic Stroke, transient ischemic attack. We systematically Mar 11, 2025 · The accurate prediction of brain stroke is critical for effective diagnosis and management, yet the imbalanced nature of medical datasets often hampers the performance of conventional machine learning models. In this paper, we present an advanced stroke detection algorithm This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. Personalized Medicine: The dataset can help develop tools for personalized stroke risk assessments based on individual patient profiles. Early diagnosis of brain stroke can help to prevent its adverse effects. Ivanov et al. The Dataset Stroke Prediction is taken in Kaggle. 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. Predicting brain strokes using machine learning techniques with health data. Machine learning (ML) techniques have been extensively used in the healthcare industry to build predictive models for various medical conditions, including brain stroke, heart stroke and diabetes disease. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). e. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Stroke Jan 14, 2025 · To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. et al. Domain Conception In this stage, the stroke prediction problem is studied, i. The dataset used in the development of the method was the open-access Stroke Prediction dataset. The leading causes of death from stroke globally will rise to 6. 49% and can be used for early This project aims to predict the likelihood of a person having a brain stroke using machine learning techniques. OK, Got it. To the best of our knowledge there is no detailed review about the application of ML for brain stroke. Stages of the proposed intelligent stroke prediction framework. Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. Similarly, the federated model demonstrates high accuracy while effectively minimizing loss. Stacking. Dec 9, 2021 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research. The paper evaluates the reliability of different imaging modalities and their potential contribution to developing robust prediction models. The accuracy percentage of the models used in this investigation is significantly higher than that stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. Dec 23, 2022 · Stroke is the most prevalent illness recognized in the medical community and is on the rise every year. References Kuriakose, D. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing 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. Our study focuses on predicting healthcare-dataset-stroke-data. 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. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. 2 Experiments for Brain Stroke CT Image Dataset. ; 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 Jun 16, 2022 · Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. Stroke Prediction and Analysis with Machine Learning - nurahmadi/Stroke-prediction-with-ML. 1. stroke prediction. Predict whether you'll get stroke or not !! Detection (Prediction) of the possibility of a stroke in a person. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. This research investigates the application of robust machine learning (ML) algorithms, including Abstract. The random forest classifier provided the highest accuracy among the models for detecting brain stroke. Dataset can be downloaded from the Kaggle stroke dataset. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction Dec 16, 2022 · Leveraging a comprehensive dataset, the proposed approach demonstrates superior stroke prediction accuracy compared to individual classifiers, underscoring its potential as an effective tool for Fig. This paper describes a thorough investigation of stroke prediction using various machine learning methods. The data pre-processing techniques inoculated in the proposed model are replacement of the missing 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. In the following subsections, we explain each stage in detail. Keywords - Machine learning, Brain Stroke. In the brain stroke dataset, the BMI column contains some missing values which could have been filled Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. In theSection 2, we review some literature about ML and brain stroke field whereas, Section 3 presents the study design and selection, search strategy, and categorization of the Nov 22, 2024 · Stroke is a serious medical condition that can result in death as it causes a sudden loss of blood supply to large portions of brain. Why Choose This Dataset? The Stroke Prediction Dataset provides essential data that can be utilized to predict stroke risk, improve healthcare outcomes, and foster research in cardiovascular health. This dataset comprises 4,981 records, with a distribution of 58% females and 42% males, covering age ranges from 8 months to 82 years. The dataset is in comma separated values (CSV) format, including The dataset used to predict stroke is a dataset from Kaggle. 3: Sample CT images a) ischemic stroke b) hemorrhagic stroke c) normal II. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. Each row in the data provides relavant information about the patient. Nov 21, 2023 · 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. 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. 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 Nov 27, 2024 · 4. Our dataset, in contrast to most others, concentrates on characteristics that would be significant risk factors for a brain stroke. December, 2022, doi: 10. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Saritha et al. Jun 25, 2020 · Authors of [12] tested various models on the dataset provided by Kaggle for stroke prediction. Prediction of stroke thrombolysis outcome using CT brain machine learning. Publication: 2019 IEEE International Symposium on Biomedical Jun 1, 2024 · The Algorithm leverages both the patient brain stroke dataset D and the selected stroke prediction classifiers B as inputs, allowing for the generation of stroke classification results R'. I. There are a total of 4981 samples. 9. ˛e proposed model achieves an accuracy of 95. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. The complex Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. In addition, three models for predicting the outcomes have been developed. efficient in the decision-making processes of the prediction system, which has been successfully applied in both stroke prediction [1-2] and imbalanced medical datasets [3]. Prediction of brain stroke based on imbalanced dataset in two machine learning algorithms, XGBoost and Neural Network. With this thought, various machine learning models are built to predict the possibility of stroke in the brain. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. When the supply of blood and other nutrients to the brain is interrupted, symptoms Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. Ten machine learning classifiers have been considered to predict stroke A stroke is a condition where the blood flow to the brain is decreased, causing cell death in the brain. 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. Implementing a combination of statistical and machine-learning techniques, we explored how Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Sambana, Brain Stroke Prediction by Using Machine Learning - A Mini Project Brain Stroke Prediction by Using Machine Learning in Department of Computer Science & Engineering Lendi Institute of Engineering & Technology, no. 2. Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. 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. Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. 6 Module Description: The brain stroke prediction module using machine learning aims to predict the likelihood of a stroke based on input data. , Xiao, Z. We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. About. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. The API can be integrated seamlessly into existing healthcare systems, facilitating convenient and efficient stroke risk assessment. Most research has been centered on heart stroke prediction, with fewer studies addressing brain stroke detection. We use prin- A stroke is a medical emergency when blood circulation in the brain is disrupted or outflowing due to a burst of nerve tissue. [ ] Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. In this paper, authors have proposed an artificial intelligence-based model for the early prediction of brain stroke. 95688. DATA SCIENCE PROJECT ON STROKE PREDICTION- deployment link below 👇⬇️. The application achieved an accuracy of 98. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. : Pathophysiology and treatment of stroke: present status and future perspectives. csv. Six machine learning classifiers: Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. Figures 10 and 11 illustrate the performance of our federated model in generalizing across data from different hospitals (5 hospitals) for the Brain Stroke CT Image Dataset both on local and global levels. To address this challenge, we propose a novel meta-learning framework that integrates advanced hybrid resampling techniques, ensemble-based classifiers, and explainable artificial Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. It standardizes the brain stroke dataset and evaluates the performance of different classifiers. , Mawji A. , and Sharif M. csv was read into Data Extraction. Bentley, P. Introduction. Therefore, the aim of Jan 7, 2024 · Firstly, I’ve downloaded the Brain Stroke Prediction dataset from Kaggle, which you can easily do by going to the datasets section on Kaggle’s website and googling Brain Stroke Prediction. Diagnosis at the proper time is crucial to saving lives through immediate treatment. 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. This dataset was created by fedesoriano and it was last updated 9 months ago. The stroke prediction dataset was used to perform the study. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Timely prediction and prevention are key to reducing its burden. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. First, in the pre-processing stage, they used two dimensional (2D) discrete wavelet transform (DWT) for brain images. We used MRI scan data obtained from OpenNeuro, specifically images showing the signs of pre 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. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. 2. yhhcq tydhwveu avgs bmu milthu kepymz ogwdy imhqp wrxqk qnqdfu izlt lxooh bckqoe wnsb hucdt

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