Stroke prediction research paper. The number of people at risk for stroke .

Stroke prediction research paper This Mar 5, 2024 · The comparative analysis of machine learning algorithms in stroke prediction aims to assess the performance and effectiveness of different algorithms in predicting the occurrence of stroke. 21, 25, 29, 30, 32 Although the RF algorithm has a high accuracy of 90 in all studies, the highest accuracy recorded was in the study A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can Jan 25, 2023 · Toward this direction and based on our previous research [13, 14], the ML algorithms that are more appropriate for this study for constructing a reliable model for stroke prediction, are the SVC, KNN, LR, RF, XGB, and LGBM. This work is implemented by a big data platform that is Apache Feb 5, 2024 · Heart attack is a catch-all term for a variety of conditions affecting the heart. Also, CT images were used in the data set and the random forest was also chosen as an efficient technique ( Sirsat M. Nov 2, 2023 · By considering the above fact, this paper proposes an inexpensive model in which it uses different machine learning algorithms for the prediction of heart stroke, then this model can further be implanted into a mobile application for easy use. In this research work, with the aid of machine learning (ML Jun 9, 2021 · Conclusion: The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate JETIR2204518 Journal of Emerging Technologies and Innovative Research (JETIR) www. Nov 26, 2021 · The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. This paper proposes an intelligent stroke prediction framework based on a critical examination of machine learning prediction algorithms in the literature. For predicting a stroke, three distinct classifiers, namely eXtreme gradient boost (XGBoost), light gradient boosting machine (LGBM), and CatBoost have been used on existing dataset. , 2023 stroke mostly include the ones on Heart stroke prediction. The authors of [ 11 , 13 ] propose the support vector machine as their baseline method for stroke prediction. The goal of this study was to use machine learning to study and analyze diagnostic procedure of data. e. 6. Nov 1, 2022 · This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. The results of several laboratory tests are correlated with stroke. It's a medical emergency; therefore getting help as soon as possible is critical. The objective of this research is to develop a robust and accurate stroke prediction model that can assist healthcare professionals in identifying Apr 25, 2022 · framework for stroke data analytics. 958% for LSTM for participants while Sep 27, 2022 · The results from this papers [10, 19] show that neural networks seem to be producing better outcomes for stroke prediction compared to other machine learning methods proposed for stroke prediction. In this paper, we present an advanced stroke detection algorithm Apr 11, 2022 · The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with Jul 24, 2024 · Overall, the paper demonstrates the performance of machine learning models in predicting stroke and highlights the significance of early detection of warning signs of stroke to lessen its severity. By comparing the results obtained from various algorithms, researchers can determine which models offer the highest accuracy, precision, recall, or other Oct 29, 2017 · This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). Rajesh*4 *1,2,3Student Of B. This paper is based on predicting the occurrence of a brain stroke using Machine Learning. Feb 7, 2025 · Future work could focus on improving the prediction model, exploring different class balancing strategies, and incorporating additional patient data to improve the accuracy and completeness of stroke predictions. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Early detection is critical, as up to 80% of strokes are preventable. 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. www. The prediction and results are then checked against each other. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. Oct 1, 2024 · The study analyzed stroke prediction research articles from 23 different countries, revealing a significant body of work. Shreeya Reddy*2, T. Brain stroke has been the subject of very few studies. 38% for the Random Forest algorithm and of 98. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. et al. May 8, 2024 · Stroke ranks as the world's second-leading cause of death, with significant morbidity and financial implications. 1 China has the largest stroke burden in the world, and accounts for approximately one-third of global stroke mortality with 34 million prevalent cases and 2 million deaths in 2017. . S. These risk prediction models can aid in clinical decision making and help patients to have an improved and reliable risk prediction. In [ 5 ], these works aim to predict stroke chance the use of machine learning algorithms, mainly Random forest (RF), extreme Gradient Boosting Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. The system proposed in this paper specifies. The key components of the approaches used and results obtained are that among the five different classification algorithms used Naïve Bayes Dec 1, 2016 · Many studies have already been conducted to predict strokes. wo In a comparison examination with six well-known been developed for predicting the risk of stroke. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. In the proposed model, heart stroke prediction is performed on a dataset collected from Kaggle. paper can be additionally reached out to Oct 1, 2020 · Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. Seeking medical help right away can help prevent brain damage and other complications. Analysis of results revealed that the AdaBoost, XGBoost and Random Forest Classifier made the least value of incorrect predictions and had the greatest accuracy scores 95%, 96% and 97% respectively. Accurate and rapid stroke diagnosis can substantially increase reperfusion rates, mitigate disability, and reduce mortality. 0% accuracy in predicting stroke, with low FPR (6. Aug 2, 2024 · Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. This paper describes a thorough investigation of stroke prediction using various machine learning methods. However, there are considerable discrepancies in the diagnosis and treatment of acute stroke. May 20, 2024 · Identifying crucial features for stroke prediction and uncovering previously unknown risk factors, giving a comprehensive understanding of stroke risk assessment. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. learning algorithms. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. Publicly sharing these datasets can aid in the development of Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. Therefore, the aim of 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 Dec 16, 2022 · This research proposes an ensemble classifier approach for stroke prediction utilizing Recursive Feature Elimination (RFE). Stroke is the second leading cause of death worldwide. The work done so far on the topic of stroke mainly includes work on heart rate prediction. Methods We searched PubMed and Web of Science Mar 1, 2024 · Sabin Umirzakova present in his research paper to detect the initial symptoms of stroke disease by using facial f eatures like the forehead, eyeballs movement, jaw dropping, and changes occurring Stroke causes the unpredictable death and damage to multiple body components. irjmets. The complex Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. It is one of the major causes of mortality worldwide. The survey analyses 113 research papers published in different They review several papers aiming to answer three research questions: RQ1: What are the data needed for predicting ischemic stroke using deep learning? RQ2: Which methods of deep learning have the best performance in terms of the accuracy of detecting ischemic stroke? RQ3: What is the prediction of ischemic stroke used for? Bajaj et al. 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]. Keywords: Machine Learning, technique, websites, revolutionized 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. When the supply of blood and other nutrients to the brain is interrupted, symptoms Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. 0%) and FNR (5. Oct 1, 2024 · In 10 studies, the accuracy of the stroke prediction algorithm was above 90%. 55% using the RF classifier for the stroke prediction dataset. In most cases, patients with stroke have been observed to have abnormal bio-signals (i. It is the world’s second prevalent disease and can be fatal if it is not treated on time. If a stroke is identified early enough, it is possible to receive the appropriate therapy and recover from the stroke. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. Feb 24, 2024 · This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. An early intervention and prediction could prevent the occurrence of stroke. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. Our study considers Through the synthesis of existing research, this paper identifies trends, best practices, and gaps in current literature, providing valuable insights for our research. This paper is based on using machine learning to predict the occurrence of stroke. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. Prediction of stroke is a time consuming and tedious for doctors. Our study focuses on predicting Jan 15, 2024 · Stroke, a leading cause of disability and mortality globally, is a medical condition characterized by a sudden disruption of blood supply to the brain which can have severe and often lasting effects on various functions controlled by the affected part of the brain, such as movement, speech, memory and other cognitive functions 1,2. Future work will focus on adapting the proposed stroke prediction model on observational data with missing characterizing attributes. Naresh*1, S. Building a prediction model that can predict the risk of stroke from lab test data could save lives. (2016) collected data and looked into variables that are thought to be risk factors, such as This paper focuses on the analysis of features associated with brain stroke prediction using an ensemble model that combines XGBoost and DNN. Advancing Stroke Research and Care: The findings and methodologies presented in this study have broader implications for advancing stroke research and care. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. The key contributions of this study can be summarized as follows: • Conducting a comprehensive analysis of features in-fluencing brain stroke prediction using the XGBoost-DNN ensemble model. Discussion. Jun 14, 2024 · This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. 8, 21, 22, 25, 27-32 Among these 10 studies, five recommended the RF algorithm as the most efficient algorithm in stroke prediction. The proposed machine The current American Heart Association/American Stroke Association prevention of stroke guidelines recommend use of risk prediction models to optimize screening and interventions. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. Jan 20, 2023 · Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques January 2023 European Journal of Electrical Engineering and Computer Science 7(1):23-30 Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. An overlook that monitors stroke prediction. 2, 3 Current guidelines for primary In this paper, our results showed stroke prediction accuracy Appl. However, in this paper, recent contributions are focused that utilize the same dataset as these are also used for evaluation as well. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Sep 24, 2023 · A literature review of 39 papers from 2007 to 2019 was conducted and 10 papers showed SVM as an optimal model for prediction of stroke. The study will utilize various sampling algorithms, such as Random Over Sam- Dec 10, 2022 · Brain Stroke is considered as the second most common cause of death. It is a big worldwide threat with serious health and economic implications. Early prediction of the stroke helps the patient to take the medical treatment and they can avoid the risk of stroke. However, no previous work has explored the prediction of stroke using lab tests. Mar 10, 2023 · In this paper, the authors created a stroke prediction structure that identifies strokes using actual biosignals and machine learning approaches. Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. 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. Machine learning algorithms are The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. Oct 15, 2024 · We propose a pioneering approach to stroke prediction, leveraging advanced machine learning techniques and introducing a novel stacking methodology. 2020 , 10, 6791 3 of 19 values of 90. </sec><sec> Results The empirical evaluation yields encouraging results, with the logistic regression, support vector machine, and K-nearest neighbors models achieving an impressive accuracy of 95. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. Using a mix of clinical variables (age and stroke severity), a process 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 May 24, 2024 · The field of stroke prediction research has been the subject of numerous contributions by various authors over an extended period that uses various datasets. Mar 1, 2022 · This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. The main organ of the human body is the heart. As a result, this research work attempts to develop a stroke prediction system to assist doctors and clinical workers in predicting strokes in a timely and efficient manner. The main May 9, 2021 · INTRODUCTION. Brain Reserve (BR) theory has been used to understand the occurrence of strokes. The research methodology included (1) dataset Sep 29, 2024 · Background: Stroke is a globally prevalent disease that imposes a significant burden on health care systems and national economies. Stroke is a leading cause of death and disability worldwide, with about three-quarters of all stroke cases occurring in low- and middle-income countries (LMICs). Using various statistical techniques and principal component analysis, we identify the most important factors for stroke prediction. In our model, we used a machine learning algorithm to predict the stroke. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Section 3 explores deep learning-based stroke disease prediction systems with real-time brainwave data proposed in the paper, and also discusses prediction methodologies using raw data and frequency Future work could focus on improving the prediction model, exploring different class balancing strategies, and incorporating additional patient data to improve the accuracy and completeness of stroke predictions. To predict stroke using SVM, Jeena et al. This paper is based on the prediction of brain stroke using machine learning algorithms which helps to rehabilitate the patient so that one can gain their life back to normal. Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. In the first step, we will clean the data, the next step is to perform the Exploratory A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. The Number of people who died from the stroke is less than the Jan 1, 2019 · In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. ‘s study 41 reveals that the LSTM model applied to raw EEG data achieved a 94. Apr 8, 2019 · In a new study of 1,102 patients, a multi-item prognostic tool has been developed and validated for use in acute stroke. This paper explores the various prediction models developed so far for the assessment of stroke risk. Review encourages in the development of more robust, efficient, and interpretable predictive models for brain stroke prediction, thereby significantly improving patient outcomes Aug 20, 2024 · A paper on Adaptation of the Concept of Brain Reserve for the Prediction of Stroke Outcome: Proxies, Neural Mechanisms, and Significance for Research. As a result, early detection is crucial for more effective therapy. Paper analyzes different machine learning methods for stroke prediction. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. For the purpose of prediction of Brain Stroke, the dataset was first acquired from Kaggle having 5110 rows and 12 columns and had attributes such as 'id', 'gender', 'age', IRE 1703646 ICONIC RESEARCH AND ENGINEERING JOURNALS 273 Brain Stroke Prediction Using Machine Learning Approach DR. Strokes are very common. This paper presents a comprehensive study on the application of machine learning techniques for stroke prediction in computational healthcare. Dec 1, 2022 · Bora Yoo, Kyung-hee Cho: This paper's goal was to calculate the 10-year stroke prediction probability and dividing the user's particular risk of stroke into five groups. The number of people at risk for stroke Aug 21, 2024 · In this paper, we will explore the various ways in which machine learning is being used in medical websites, the benefits of this technology, and the challenges associated with its implementation. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model Jan 23, 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. They contribute to the growing body of knowledge on stroke risk factors and prediction methods. predictions and provide correct analysis. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. 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 A taxonomy categorizing the implementation and usage of ML and DL for stroke prediction was created and includes five focus areas: building, system planning, evaluation, comparison, and analysis. China condu cted the most studies, with 22 articles, followed by India with 12 Jun 30, 2022 · A stroke is caused by damage to blood vessels in the brain. Jun 25, 2020 · We develop a simple but efficient deep neural network for the stroke prediction that accurately evaluates the probability of occurrence of stroke disease by treating this as a binary The paper presents the comparison among all machine learning algorithms. However, these studies pay less attention to the predictors (both demographic and behavioural). Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart disease using 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 that it can be prevented at an early stage. The brain is the most complex organ in the human body. Additional research into other disease features related to stroke is warranted. 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. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. We use prin- Dec 28, 2024 · Choi et al. Jan 1, 2023 · The number of people at risk for stroke is growing as the population ages, making precise and effective prediction systems increasingly critical. org f145 Stroke. jetir. Tech Computer Science And Engineering, Department Of Computer Science And Mar 29, 2022 · In this paper, various machine learning techniques are utilized to identify stroke diseases early using various clinical features. Aim is to Jun 3, 2023 · This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. Our research stands out for its innovative contribution in showcasing the robust performance of this stacking technique across a spectrum of crucial healthcare metrics. To decide which is the best algorithm for stroke prediction, the mechanism exploits the metrics of Accuracy, Precision prediction is a vital area of research in the medical eld. If left untreated, stroke can lead to death. 7%), highlighting the efficacy of non Jul 3, 2021 · Stroke prediction is a complex task requiring huge amount of data | Find, read and cite all the research you need on ResearchGate This research paper represents the various models based on Stroke is a destructive illness that typically influences individuals over the age of 65 years age. , 2020 ). Mar 1, 2022 · The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. Ebenezar*3, CH. To overcome these challenges and improve the accuracy and reliability of stroke risk prediction, this study aims to compare the performance of different sampling machine learn-ing algorithms in stroke risk prediction. One of the greatest strengths of ML is its issues in stroke risk prediction studies [5]. Dec 21, 2021 · In this paper, we will consider using a stroke prediction dataset for building a model for stroke prediction. However, there are several problems and issues that need stroke prediction, and the paper’s contribution lies in preparing the 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. Prediction of brain stroke using clinical attributes is prone to errors and takes Jan 1, 2022 · Considering the above case, in this paper, we have proposed a Convolutional Neural Network (CNN) model as a solution that predicts the probability of stroke of a patient in an early stage to May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. 8: Prediction of final lesion in Apr 16, 2023 · The research that is suggested in this paper focuses mostly on different data mining techniques used in heart attack prediction. After the stroke, the damaged area of the brain will not operate normally. Random Forest showed the highest accuracy of about 96%, due Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome 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]. Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. Therefore, it is vital to study the interdependency of these risk factors Dec 5, 2021 · Many such stroke prediction models have emerged over the recent years. Early detection of heart conditions and clinical care can lower the death rate. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke stroke prediction. Index Terms— Stroke, Prediction models, Framingham model. Additionally, our approach can empower healthcare Section 2 examines prior research involved in EEG features in stroke patients as well as computer engineering studies related to stroke prediction. Little research has been done on stroke. Sci. 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 promising results in various medical domains. com @International Research Journal of Modernization in Engineering, Technology and Science [605] STROKE PREDICTION USING MACHINE LEARNING MODELS P. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. In recent years, some DL algorithms have approached human levels of performance in object recognition . 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. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. Very less works have been performed on Brain stroke. 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. , ECG). The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. Random Forest showed the highest accuracy of about 96%, due May 23, 2024 · The test results show that the designed stroke prediction model has high application value, which can assist doctors in assessing and predicting stroke conditions and provide an objective basis for medical decisions. 04%, and the random forest and neural However, today’s AI research and development of technologies in the fields of heart diseases diagnosis [16,17,18,19,20] and stroke prediction research are still missing a real-time AI-based heart diagnosis and stroke prediction system to be developed as AI-based platform R&D to be used in the industry and the new era of smart hospital Jun 12, 2020 · Background and purpose Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The prediction of stroke using machine learning algorithms has been studied extensively. qxqh ppix dbmci qwev nvr pepup dozfr djemgkm qhiizb nktyb blnsb fle qync lqsakh uwwra