- Stroke prediction dataset In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. Oct 21, 2024 · Reading CSV files, which have our data. 6 shows the graphical representation of the imbalanced data as well as balanced data. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Jan 5, 2024 · Prior studies on stroke prediction datasets have not elucidated the rationale behind model predictions. We systematically Brain stroke prediction dataset A stroke is a medical condition in which poor blood flow to the brain causes cell death. Through This project aims to predict the likelihood of stroke using a dataset from Kaggle that contains various health-related attributes. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. However, these studies pay less attention to the predictors (both demographic and behavioural). The project covers data cleaning, visualization, parameter tuning, and explainable AI techniques. A. Oct 29, 2017 · The Cox proportional hazards model and machine learning approach have been compared for stroke prediction on the Cardiovascular Health Study (CHS) dataset . Dec 15, 2022 · State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. It gives users a quick understanding of the dataset's structure. 5% accuracy, emphasizing the importance of selecting the right algorithm for a specific dataset. Early identification of stroke is crucial for intervention, requiring reliable models. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). This web page presents a project that analyzes a stroke dataset from Kaggle and uses various machine learning methods to predict the risk of stroke. This project uses machine learning to predict brain strokes by analyzing patient data, including demographics, medical history, and clinical parameters. 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]. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. As a result, early detection is crucial for more effective therapy. PySpark is used to build a predictive model to analyse the Jun 25, 2020 · Authors of [12] tested various models on the dataset provided by Kaggle for stroke prediction. Jan 24, 2022 · The objective of this research is to apply three current Deep Learning (DL) approaches for 6-month IS outcome predictions, using the openly accessible International Stroke Trial (IST) dataset. head(10) ## Displaying top 10 rows data. 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. To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in Sep 27, 2022 · The quality of the Framingham cardiovascular study dataset makes it one of the most used data for identifying risk factors and stroke prediction after the Cardiovascular Heart Disease (CHS) dataset . 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 Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. ipynb源代码。 运行项目进行评估 克隆存储库。 The Dataset Stroke Prediction is taken in Kaggle. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. In this project, we decide to use “Stroke Prediction Dataset” provided by Fedesoriano from Kaggle. 2. The utilization of publicly available datasets, such as the Stroke Prediction Dataset, offers several advantages. In the following subsections, we explain each stage in detail. 2. Kaggle is an AirBnB for Data Scientists. Domain Conception In this stage, the stroke prediction problem is studied, i. OK, Got it. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze May 1, 2024 · This study proposed a hybrid system for brain stroke prediction (HSBSP) using data from the Stroke Prediction Dataset. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Many such stroke prediction models have emerged over the recent years. Jul 28, 2021 · We developed prediction models for the number of heatstroke cases using the datasets between 1 June and 30 September between 2015 and 2017 as the training dataset. We proposed an efficient retinal image representation together with clinical information to capture a comprehensive overview of cardiovascular health, leveraging large multimodal datasets for new medical insights. 77% to 88. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. Large neuroimaging datasets are increasingly being used to identify novel brain-behavior relationships in stroke rehabilitation research 1,2. A balanced sample dataset is created by combining all 209 observations with stroke = 1 and 10% of the observations with stroke = 0 which were obtained by random sampling from the 4700 observations. Stroke risk now follows a sigmoidal curve (sharp increase after age 50), reflecting real-world epidemiological trends. 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. csv at master · fmspecial/Stroke_Prediction Libraries Used: Pandas, Scitkitlearn, Keras, Tensorflow, MatPlotLib, Seaborn, and NumPy DataSet Description: The Kaggle stroke prediction dataset contains over 5 thousand samples with 11 total features (3 continuous) including age, BMI, average glucose level, and more. The data pre-processing techniques inoculated in the proposed model are replacement of the missing Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. The dataset used in the development of the method was the open-access Stroke Prediction dataset. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can Dataset containing Stroke Prediction metrics. Unfortunately, some samples younger Apr 25, 2022 · intelligent stroke prediction framework that is based on the data analytics lifecycle [10]. Jan 25, 2023 · The title of this episode is: “Tabular Classification with a Stroke Prediction Dataset”. These metrics included patients’ demographic data (gender, age, marital status, type of work and residence type) and health records (hypertension, heart disease, average glucose level measured after meal, Body Mass Index (BMI), smoking status and experience of stroke). ere were 5110 rows and 12 columns in this dataset. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). Stroke Prediction Module. The Brain MRI Segmentation and ISLES datasets are critical image datasets for training algorithms to identify and segment brain structures affected by strokes. The model could help improve a patient’s outcomes. Discussion. , ECG). Aug 29, 2024 · An algorithm for stroke prediction has been developed by Singh et al. Stroke Prediction Dataset|中风预测数据集|医疗健康数据集 收藏 Sep 21, 2021 · To do this, we'll use the Stroke Prediction Dataset provided by fedesoriano on Kaggle. Ratings are also boosted by 1%–2% for accuracy, recall, F1, and AUC. With my interest in healthcare and parents aging into a new decade, I chose this Stroke Prediction Dataset from Kaggle for my Python project. 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. In most cases, patients with stroke have been observed to have abnormal bio-signals (i. In the context of stroke prediction using the Stroke Prediction Dataset, various machine learning models have been employed. 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. , ischemic or hemorrhagic stroke [1]. The stroke prediction dataset was pre-processed by handling missing Nov 18, 2024 · The research was carried out using the stroke prediction dataset available on the Kaggle website. The research methodology included (1) dataset This report presents an analysis aimed at developing and deploying a robust stroke prediction model using R. Learn more. 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. stroke prediction within the realm of computational healthcare. If left untreated, stroke can lead to death. Exploratory Data Analysis & Pre Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. We employ multiple machine learning and deep learning models, including Logistic Regression, Random Forest, and Keras Sequential models, to improve the prediction accuracy. This RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. Ivanov et al. This experiment was also conducted to compare the machine learning model performance between Decision Tree, Random Apr 16, 2023 · It is necessary to automate the heart stroke prediction procedure because it is a hard task to reduce risks and warn the patient well in advance. 24 and 0. machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks tensor kaggle-dataset stroke-prediction Updated Mar 30, 2022 Python 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}. 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 Aug 1, 2023 · Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. openresty Nov 1, 2022 · The used dataset in this study for stroke prediction is highly asymmetry which influences the result. Initially an EDA has been done to understand the features and later Apr 18, 2023 · In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The current American Heart Association/American Stroke Association prevention of stroke guidelines recommend use of risk prediction models to optimize screening and interventions. Healthcare professionals can discover Aug 20, 2024 · The contributions of this work are two-fold: first, we introduce a standardized benchmarking of final stroke infarct segmentation algorithms through the ISLES’24 challenge; second, we provide insights into infarct segmentation using multimodal imaging and clinical data strategies by identifying outperforming methods on a finely curated dataset. There were 5110 rows and 12 columns in this dataset. The system proposed in this paper specifies. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. The Pearson correlation heatmap , which investigates the linear relationship between all of the features, is depicted in Figure 3. 55% using the RF classifier for the stroke prediction dataset. Flower allows us to implement clients, simulate a server, and provide special simulation capabilities that create instances of FlowerClient only when needed for Oct 4, 2024 · The authors in 22 used the Cardiovascular Health Study dataset to evaluate two stroke prediction methods: the Cox proportional hazards model and a machine learning technique (CHS). Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome Summary. The dataset was obtained from "Healthcare dataset stroke data". First, it allows for the reproducibility and transparency Synthetically generated dataset containing Stroke Prediction metrics. Deep learning is capable of constructing a nonlinear Model comparison techniques are employed to determine the best-performing model for stroke prediction. This study evaluates three different classification models for heart stroke prediction. In recent years, some DL algorithms have approached human levels of performance in object recognition . This project predicts stroke disease using three ML algorithms - Stroke_Prediction/Stroke_dataset. A dataset containing all the required fields to build robust AI/ML models to detect Stroke. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Jan 1, 2024 · Our clinical dataset included the following features: age, gender, wake-up (whether the patient experienced symptoms at waking up), arterial fibrillation (binary), whether the patient was referred from another hospital, National Institutes of Health Stroke Scale (NIHSS) score at presentation, Time-To-Hospital (TTH), whether treated via Stroke Risk Prediction Dataset – Clinically-Inspired Symptom & Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 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 May 23, 2024 · In fact, (1) the average age of stroke patients is much higher than the average age of those who do not suffer from stroke disease, and due to the decreased immunity of the elderly, the risk of suffering from various diseases will be higher; (2) the average blood glucose of stroke patients is higher, and the results of related studies have Feb 7, 2024 · Their objectives encompassed the creation of ML prediction models for stroke disease, tackling the challenge of severe class imbalance presented by stroke patients while simultaneously delving into the model’s decision-making process but achieving low accuracy (73. No records were removed because the dataset had a small subset of missing values and records logged as unknown. This dataset contains some obvious outliers and noises, such as age and BMI items. e. 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. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. 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]. Dataset. data=pd. Working with a real-world dataset, you’ll use R to load, clean, process, and analyze the data and then train multiple classification models to determine Brain stroke prediction dataset. It's a medical emergency; therefore getting help as soon as possible is critical. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', 'Work Type Oct 25, 2023 · Stroke prediction plays a crucial role in preventing and managing this debilitating condition. Objective stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. The dataset is designed for machine learning and research, and includes features like age, gender, hypertension, heart disease, and smoking status. The number 0 indicates that no stroke risk was identified, while the value 1 indicates that a stroke risk was detected. - ebbeberge/stroke-prediction Nov 26, 2021 · Dataset. View Notebook Download Dataset 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. We use prin- Stroke dataset for better results. Jan 15, 2024 · Stroke risk dataset: Stroke risk datasets play a pivotal role in machine learning (ML) for predicting the likelihood of a stroke. 3. Stages of the proposed intelligent stroke prediction framework. 9. Lesion location and lesion overlap with extant brain structures and networks of interest are consistently reported as key predictors of stroke outcomes 3–6. Our study considers Jan 26, 2021 · 11 clinical features for predicting stroke events. In conjunction Balance dataset¶ Stroke prediction dataset is highly imbalanced. Our work aims to improve upon existing stroke prediction models by achieving higher accuracy and robustness. describe() ## Showing data's statistical features The aims of this study were to (i) compare Cox and ML models for prediction of risk of stroke in China at varying intervals of follow-up (ie, stroke within 9 years, 0–3 years, 3–6 years, 6–9 years); (ii) identify individuals for whom ML models might be superior to conventional Cox-based approaches for stroke risk prediction; and (iii Aug 22, 2023 · 303 See Other. g. A recent figure of stroke-related cost almost reached $46 billion. Prediction of brain stroke based on imbalanced dataset in two machine learning algorithms, XGBoost and Neural Network neural-network xgboost-classifier brain-stroke-prediction Updated Jul 6, 2023 Mar 18, 2021 · For this walk-through, we’ll be using the stroke prediction data set, but having already lost a day to trying and tuning different models for this dataset, I will recommend using a random 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. Therefore, the aim of Oct 19, 2022 · Stroke Prediction Dataset have been used to conduct the proposed experiment. 1 Cerebral Stroke Prediction Dataset (CSP) In this study, the CSP dataset sourced from Kaggle was utilized to predict stroke disease. Nov 22, 2024 · 2. Jun 14, 2024 · This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. The dataset D is initially divided into distinct training and testing sets, comprising 80 % and 20 % of the data, respectively. 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. 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. , hypertension, chest pain) scale with age (see Medical Validity). There are only 209 observation with stroke = 1 and 4700 observations with stroke = 0. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. “The prime objective of this project is to construct a prediction model for predicting stroke using machine learning algorithms. 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. Specially, they considered the common problems of prediction in medical dataset, feature selection, and data imputation. The API can be integrated seamlessly into existing healthcare systems Among these, the Stroke Prediction Dataset is essential for developing tabular predictive models focused on risk assessment and early warning signs of stroke. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. Purpose of dataset: To predict stroke based on other attributes. Nov 8, 2024 · Abstract. The dataset is in comma separated values (CSV) format, including Download the Stroke Prediction Dataset to predict stroke risk based on various health and lifestyle factors. Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. The target, stroke, is a binary variable and so classification methods are needed to predict the probability of stroke. Nov 8, 2023 · About Data Analysis Report. 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. Seeking medical help right away can help prevent brain damage and other complications. 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. According to the methods and standards from MONICA 3 [42], the minimum age of stroke-monitoring should be 25. x = df. Title: Stroke Prediction Dataset. Feature Selection: The web app allows users to select and analyze specific features from the dataset. Apr 20, 2023 · Stroke Prediction Dataset have been used to conduct the proposed experiment. It consists of 5110 observations and 12 variables Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. 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. We use principal component analysis (PCA) to transform the higher dimensional feature space into a lower dimension subspace, and understand the relative importance of each input attributes. The leading causes of death from stroke globally will rise to 6. . The output attribute is a The "Stroke Prediction Dataset" includes health and lifestyle data from patients with a history of stroke. Users may find it challenging to comprehend and interpret the results. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing Stroke Prediction and Analysis with Machine Learning - nurahmadi/Stroke-prediction-with-ML. Sep 22, 2023 · About Data Analysis Report. stroke prediction. With help of this CSV, we will try to understand the pattern and create our prediction model. Our study focuses on predicting May 24, 2024 · The stroke prediction dataset was created by McKinsey & Company and Kaggle is the source of the data used in this study 38,39. Each row in the data provides relavant information about the patient. The latest dataset is updated on 2021 with 5111 instances and 12 attributes. The cardiac stroke dataset is used in this work to study the inter-dependency of different risk factors of stroke. After the stroke, the damaged area of the brain will not operate normally. drop(['stroke'], axis=1) y = df['stroke'] 12. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% Aug 22, 2021 · Every 40 seconds in the US, someone experiences a stroke, and every four minutes, someone dies from it according to the CDC. ipynb ： Stroke Prediction. A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. This dataset consists of 5110 instances and encompasses 12 attributes. An overlook that monitors stroke prediction. Each row in the dataset provides relavant information about the patient like Nov 27, 2024 · We used TensorFlow Federated Footnote 1 (TFF) for the tabular dataset (Stroke Prediction Dataset) and Flower framework Footnote 2 for the image dataset (Brain Stroke CT Image Dataset). Dec 14, 2023 · Dataset. Our task is to predict the probability that a patient will have a stroke. The dataset is in comma separated 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'. A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. 88%. This experiment was also conducted to compare the machine learning model performance between Decision Tree, Random Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. May 12, 2021 · The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. The dataset used to predict stroke is a dataset from Kaggle. Dataset: Stroke Prediction Dataset Mar 1, 2022 · The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. Stacking. 17 and compared to a variety of other methods on the dataset “Cardiovascular Health Study (CHS)”. Jan 9, 2025 · The results ranged from 73. We searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. The value of the output column stroke is either 1 or 0. 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. Nov 1, 2019 · In this subsection, we will use the stroke dataset to verify the prediction method for missing values in Section 3. Graph depicting attributes in the Stroke Prediction dataset (outcome 0: no stroke, outcome 1: stroke). This dataset consists of 5110 rows and 12 columns. The dataset’s objective is to estimate the probability of stroke occurring in patients using various input parameters. The benchmarks section lists all benchmarks using a given dataset or any of its 3. read_csv('healthcare-dataset-stroke-data. ˛e proposed model achieves an accuracy of 95. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Mar 15, 2024 · The prediction rate has increased between 0. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction Nov 14, 2024 · Zhu et al. The goal is to provide accurate predictions for early intervention, aiding healthcare providers in improving patient outcomes and reducing stroke-related complications. Oct 27, 2020 · The brain is an energy-consuming organ that heavily relies on the heart for energy supply. Objectives:-Objective 1: To identify which factors have the most influence on stroke prediction Nov 1, 2019 · Most of the existing researches about stroke prediction are concerned with the complete and class balance dataset, but few medical datasets can strictly meet such requirements. One of the greatest strengths of ML is its Dec 28, 2024 · This retrospective observational study aimed to analyze stroke prediction in patients. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. 57%) using Logistic Regression on kaggle dataset . Feb 7, 2025 · The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. The proposed model obtained an accuracy of 96. Dataset can be downloaded from the Kaggle stroke dataset. Their ML models achieved the highest reported accuracy for this task, pinpointing demographic and clinical factors, such as age, BMI, and marital status, as key predictors of mortality. 49% and can be used for early We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. Dec 8, 2020 · The dataset consisted of 10 metrics for a total of 43,400 patients. Brain stroke prediction dataset. e stroke prediction dataset [16] was used to perform the study. Therefore, it is vital to study the interdependency of these risk factors Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. The stroke prediction dataset was used to perform the study. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. 21227/mxfb 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. csv ：在Kaggle中找到的中风预测数据集 Stroke Prediction. In the dataset, Oct 1, 2023 · To solve this issue, future introduced a swarm intelligence-based optimization for improve classification accuracy. Prediction is done based on the condition of the patient, the ascribe, the diseases he has, and the influences of those diseases that lead to a stroke, early prediction of heart stroke risk can help in timely Intercede to minimize the risk of stroke, by making use of Machine learning algorithms, for Aug 28, 2021 · So, framing the prediction we are targeting: is a patient likely to have a stroke or not have a stroke based on the categorical data from the patient records. This paper describes a thorough investigation of stroke prediction using various machine learning methods. 3. To enhance the accuracy of the stroke prediction model, the dataset will be analyzed and processed using various data science methodologies and algorithms. ; Symptom probabilities (e. e value of the output column stroke is either 1 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. focused on mortality prediction in stroke patients using a dataset of over 7,000 individuals. Year: 2023. The dataset we employed is the Stroke Prediction Dataset, which can be accessed through the Kaggle platform. info() ## Showing information about datase data. Fig. The models are a Random Forest, a K-Nearest Neighbor and a Logistic Regression model. 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. [ ] Nov 21, 2023 · title = {Stroke Prediction Dataset}, year = {2023} } RIS TY - DATA T1 - Stroke Prediction Dataset AU - Ahmad Hassan PY - 2023 PB - IEEE Dataport UR - 10. Link: healthcare-dataset-stroke-data. Explainable AI (XAI) can explain the 档案结构 healthcare-dataset-stroke-data. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. - ajspurr/stroke_prediction Jul 1, 2021 · This study focuses on various techniques to analyse and retrieve the required information from big data in the stroke prediction dataset. The dataset under investigation comprises clinical and Background & Summary. 85% in this study when the proposed strategy is applied to the stroke prediction dataset in comparison to PCA and FA features. The dataset included 401 cases of healthy individuals and 262 cases of stroke patients admitted in hospital 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. Stroke is a common cause of mortality among older people. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. With an increased synergy between technology and medical diagnosis, caregivers create opportunities for better patient management by systematically mining and archiving the patients' medical records. We also provide benchmark performance of the state-of-art machine learning algorithms for predicting stroke using electronic health records. In this paper, we perform an analysis of patients’ electronic health records to identify the impact of risk factors on stroke prediction. 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 Aug 2, 2023 · Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. This dataset was created by fedesoriano and it was last updated 9 months ago. 3,4 Beginning in 1991, the original Framingham Stroke Risk Profile (Framingham Stroke) estimated 10-year risk of developing stroke using key risk factors identified Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. This dataset has been used to predict stroke with 566 different model algorithms. By detecting high-risk individuals early, appropriate preventive measures can be taken to reduce the incidence and impact of stroke. The analysis of the experimental results of raw data, power values, and relative values showed that using raw data achieved the highest stroke prediction accuracy. csv') data. csv. We focused on structured clinical data, excluding image and text analysis. Nov 9, 2024 · Although this research has demonstrated promising results on the Kaggle dataset for stroke prediction, future work should involve testing the model on multi-center datasets, which include data from various demographics, geographies, and healthcare systems, and longitudinal data that capture patient health metrics over a period of time. 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. Our research, however, delves into the significance of each feature and clarifies the factors influencing specific model decisions. With the advancement of technology in the medical field, predicting the occurrence of a stroke can be made using Machine Learning. Nov 13, 2022 · It is a competition on kaggle with stroke Prediction, which is heavily imbalanced. Nov 1, 2022 · Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. Age-Accurate Risk Modeling:. In this research work, with the aid of machine learning (ML Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. Jun 22, 2021 · In this paper, we propose a system that enables the early detection and prediction of stroke disease based on deep learning using EEG raw data, power values, and relative values. Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. 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. 11 clinical features for predicting stroke events. Nov 26, 2021 · 2. The developed prediction models Stroke prediction with machine learning and SHAP algorithm using Kaggle dataset - Silvano315/Stroke_Prediction. Sep 30, 2023 · In this dataset, I will create a dashboard that can be used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. For the incomplete data, a missing value imputation method based on iterative mechanism has shown an acceptable prediction accuracy [14], [15]. 52%) and high FP rate (26. To collect features, a This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. About. In order to carry out the investigation, the stroke prediction dataset is collected from UCI machine learning repository. Training a machine learning model with an imbalanced dataset gives poor performance and inaccurate results. qad amos natrzf fehq rjji xqi zfojiig ccf wupvrj ivpy ympohl ypmxaz xfpq xbkqph seua