Stroke prediction research paper. Little research has been done on stroke.

Stroke prediction research paper com @International Research Journal of Modernization in Engineering, Technology and Science [605] STROKE PREDICTION USING MACHINE LEARNING MODELS P. They are explained below: In 2014, Hamed Asadi, Richard Dowling, Bernard Yan, Peter Mitchell [1], conducted a look back study on a. Healthcare professionals can discover Stroke prediction and the future of prognosis research issues in stroke risk prediction studies [5]. This work is implemented by a big data platform that is Apache Section 2 examines prior research involved in EEG features in stroke patients as well as computer engineering studies related to stroke prediction. They contribute to the growing body of knowledge on stroke risk factors and prediction methods. As a result, early detection is crucial for more effective therapy. jetir. Mar 16, 2023 · Patient outcome prediction is critical in management of ischemic stroke. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. We use prin- May 9, 2021 · INTRODUCTION. The results in Table 4 indicate that the proposed method outperforms the existing work, achieving the highest accuracy of 92. This research offers an analysis of the factors that enhance the stroke prediction process based on electronic health records. Using various statistical techniques and principal component analysis, we identify the most important factors for stroke prediction. 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', Oct 29, 2017 · This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. The main Jun 19, 2021 · Heart Stroke is one of the severe health hazards; therefore, early heart stroke prediction helps the society to save human lives. Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. An overview of ML based automated algorithms for stroke outcome prediction is provided in Table 1 (Section B). The brain cells die when they are deprived of the oxygen and glucose needed for their survival. Machine learning algorithms are The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. ML (3) The designed deep regression model performs stroke prediction without human intervention and auto-matically outputs stroke risk prediction results in an end-to-end manner The remaining part of this paper is organized as follows. It is a big worldwide threat with serious health and economic implications. 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. It can also occur when there is a halt in the blood flow and other nutrients to the brain. Aim is to stroke mostly include the ones on Heart stroke prediction. For the offline Nov 22, 2024 · Stroke prediction demands accurate identification of individuals in the early stages of the disease, as it is crucial for effective treatment. By comparing the results obtained from various algorithms, researchers can determine which models offer the highest accuracy, precision, recall, or other 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 Apr 25, 2022 · framework for stroke data analytics. Discussion. Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can 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. 0%) and FNR (5. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. 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. 7%), highlighting the efficacy of non Oct 1, 2020 · Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. , 2023 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. Valdés-Hernández 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. Little research has been done on stroke. Our study focuses on predicting 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. May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. 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 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. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals This paper describes a thorough investigation of stroke prediction using various machine learning methods. Tech Computer Science And Engineering, Department Of Computer Science And a ADAPT SFI Research Centre, Dublin, Ireland and identify the key factors necessary for stroke prediction. Nov 1, 2022 · This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. However, no previous work has explored the prediction of stroke using lab tests. Results After screening all studies by title, abstract and conclu-sion, we found 8 studies about stroke prevention, 18 stud-ies about stroke diagnosis, 4 studies about stroke treatment, and 9 studies about stroke prognostication. Random Forest showed the highest accuracy of about 96%, due 6. 0% accuracy in predicting stroke, with low FPR (6. 8: Prediction of final lesion in A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. In addition, the majority of studies are in stroke diagnosis whereas the majority of studies are in stroke treatment, indicating a research gap that needs to be filled. In this paper, we attempt to bridge this gap by providing a systematic analysis Jun 22, 2021 · 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 . The brain is the most complex organ in the human body. 2, 3 Current guidelines for primary Feb 24, 2023 · Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate King Abdullah International Medical Research Center (KAIMRC) Minstry of National Gurad Health Affairs Riyadh, KSA Abstract—Stroke is a neurological disease that occurs when a brain cells die as a result of oxygen and nutrient deficiency. In the Mar 1, 2022 · The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. ‘s study 41 reveals that the LSTM model applied to raw EEG data achieved a 94. In response to this need, we a stroke clustering and prediction system called Stroke MD. Additionally, our approach can empower healthcare RESEARCH PAPER; TOPICS; FOR AUTHORS . Early detection of heart conditions and clinical care can lower the death rate. The results of several laboratory tests are correlated with stroke. Oct 1, 2024 · The study analyzed stroke prediction research articles from 23 different countries, revealing a significant body of work. In ten investigations for stroke issues, Support Vector Machine (SVM) was found to be the best models. In this research article, machine learning models are applied on well known heart stroke classification data-set. Brain stroke has been the subject of very few studies. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Hence, they were the best suited model for stroke prediction and can feasibly be used by physicians to predict stroke in real world. In most cases, patients with stroke have been observed to have abnormal bio-signals (i. Rajesh*4 *1,2,3Student Of B. Our research stands out for its innovative contribution in showcasing the robust performance of this stacking technique across a spectrum of crucial healthcare metrics. The work done so far on the topic of stroke mainly includes work on heart rate prediction. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome Dec 28, 2024 · Choi et al. The key components of the approaches used and results obtained are that among the five different classification algorithms used Naïve Bayes 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]. In the proposed model, heart stroke prediction is performed on a dataset collected from Kaggle. [8] Mar 4, 2025 · Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions information Muhammad Febrian Rachmadi , Maria del C. S. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Prediction of brain stroke using clinical attributes is prone to errors and takes 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 system proposed in this paper specifies. The prediction and results are then checked against each other. May 20, 2024 · Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. Furthermore, it is quite essential to understand the risk factors that make a patient more susceptible to strokes, thus there are some factors that make stroke prediction much easier. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. However, in this paper, recent contributions are focused that utilize the same dataset as these are also used for evaluation as well. for stroke prediction is covered. Table 2 shows the basic characteristics of the included studies. 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 Through the synthesis of existing research, this paper identifies trends, best practices, and gaps in current literature, providing valuable insights for our research. Oct 12, 2022 · the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. e. After the stroke, the damaged area of the brain will not operate normally. 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. It is the world’s second prevalent disease and can be fatal if it is not treated on time. This paper describes a thorough investigation of stroke prediction using various machine learning methods. The main The current American Heart Association/American Stroke Association prevention of stroke guidelines recommend use of risk prediction models to optimize screening and interventions. Brain stroke recognition using MRI reports was the subject of research by Kim et al. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. Prediction of stroke is a time consuming and tedious for doctors. Explainable AI (XAI) can explain the 2019. In the first step, we will clean the data, the next step is to perform the Exploratory Stroke is a destructive illness that typically influences individuals over the age of 65 years age. 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). Also, CT images were used in the data set and the random forest was also chosen as an efficient technique ( Sirsat M. Strokes are very common. Index Terms— Stroke, Prediction models, Framingham model. 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. According to the World Health Organization (WHO), stroke is the leading cause of death and disability globally. One of the greatest strengths of ML is its Jul 25, 2023 · Early awareness of different warning signs of stroke can minimize the stroke. 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 Feb 11, 2022 · Novel ML-driven approaches to stroke risk prediction allow researchers to overcome some of the challenges frequently associated with traditional risk prediction models. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. 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. 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. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. Shreeya Reddy*2, T. In our model, we used a machine learning algorithm to predict the stroke. This paper is based on predicting the occurrence of a brain stroke using Machine Learning. e study uses synthetic samples for training the support vector Dec 15, 2022 · State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. The authors examine research that predict stroke risk variables and outcomes using a variety of machine learning algorithms, like random forests, decision trees also neural networks. 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. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke Oct 1, 2024 · In 10 studies, the accuracy of the stroke prediction algorithm was above 90%. , 2020 ). </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. Users may find it challenging to comprehend and interpret the results. Section2describes thestroke dataset, and adetailed analysis of the stroke prediction network model was performed Jun 22, 2021 · Section 2 examines prior research involved in EEG features in stroke patients as well as computer engineering studies related to stroke prediction. [2]. previously published papers related to work on prediction of stroke types using different machine learning approaches. Naresh*1, S. Remagnino, "An Exploration on the Machine-Learning-Based Stroke Prediction Model," Frontiers in Neurology prediction is a vital area of research in the medical eld. This objective can be achieved using the machine learning techniques. Jun 14, 2024 · This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. This work is implemented by a big data platform that is Apache Mar 15, 2024 · The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. 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. Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. 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 research methodology included (1) dataset 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. Building a prediction model that can predict the risk of stroke from lab test data could save lives. Natural language processing (NLP), statistical analysis, and model-based Oct 1, 2024 · The purpose of this study is to systematically review published papers on stroke prediction using machine learning algorithms and introduce the most efficient machine learning algorithms and Many such stroke prediction models have emerged over the recent years. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. 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 May 27, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. [9] “Effective Analysis and Predictive Model of Stroke Disease using Classification Methods”-A. The prediction of stroke using machine learning algorithms has been studied extensively. wo In a comparison examination with six well-known A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Brain Reserve (BR) theory has been used to understand the occurrence of strokes. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. Dec 16, 2022 · This research proposes an ensemble classifier approach for stroke prediction utilizing Recursive Feature Elimination (RFE). org f145 Stroke. One of the greatest strengths of ML is its Jan 1, 2021 · Research paper [7] shows that the model was traine d using . Nov 26, 2021 · The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. 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. This paper is based on using machine learning to predict the occurrence of stroke. 04%, and the random forest and neural 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. irjmets. Decision Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior www. However, in healthcare datasets frequently characterized by imbalanced data distribution and missing values, accurately predicting both individuals at risk of stroke and healthy individuals poses a Aug 1, 2023 · Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. 8, 21, 22, 25, 27-32 Among these 10 studies, five recommended the RF algorithm as the most efficient algorithm in stroke prediction. 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. In this paper, a novel machine learning model is proposed for stroke outcome prediction using multimodal Magnetic Resonance Abstract: A Stroke is a health condition that causes damage by tearing the blood vessels in the brain. The main difficulty in their work is that in ML, a Feb 1, 2022 · This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. 55% using the RF classifier for the stroke prediction dataset. However, addressing hidden risk factors and achieving accurate prediction Oct 15, 2024 · We propose a pioneering approach to stroke prediction, leveraging advanced machine learning techniques and introducing a novel stacking methodology. China condu cted the most studies, with 22 articles, followed by India with 12 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 aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. Keywords: Machine Learning, technique, websites, revolutionized May 8, 2024 · Stroke ranks as the world's second-leading cause of death, with significant morbidity and financial implications. In addition, effect of pre-processing the data has also been summarized. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. Future work will focus on adapting the proposed stroke prediction model on observational data with missing characterizing attributes. 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 Feb 24, 2024 · This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. We used Cox regression analysis, adjusted for 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. In this paper, we present an advanced stroke detection algorithm 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. Therefore, the aim of Jan 3, 2025 · Objective To investigate the associations between a comprehensive set of retinal vascular parameters and incident stroke to unveil new associations and explore its predictive power for stroke risk. 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. The authors of [ 11 , 13 ] propose the support vector machine as their baseline method for stroke prediction. , who investigated machine learning techniques. predictions and provide correct analysis. 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 Jun 3, 2023 · This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. , ECG). 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 Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. However, there are several problems and issues that need stroke prediction, and the paper’s contribution lies in preparing the Jan 5, 2024 · Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. 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 Jun 30, 2022 · A stroke is caused by damage to blood vessels in the brain. Very less works have been performed on Brain stroke. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate Dec 5, 2021 · Many such stroke prediction models have emerged over the recent years. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. This paper explores the various prediction models developed so far for the assessment of stroke risk. 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. 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. These risk prediction models can aid in clinical decision making and help patients to have an improved and reliable risk prediction. Sudha, Oct 11, 2023 · Ischemic stroke is a life-threatening disorder that significantly reduces a person’s lifespan. 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]. Stroke is a common cause of mortality among older people. Dec 21, 2021 · In this paper, we will consider using a stroke prediction dataset for building a model for stroke prediction. Early prediction of the stroke helps the patient to take the medical treatment and they can avoid the risk of stroke. Stroke detection within the first few hours improves the chances Oct 3, 2023 · This paper focuses on developing a prediction model for heart stroke using age, hypertension, previous heart disease status, average body glucose level, bmi, and smoking status as parameters. 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. . Early detection is critical, as up to 80% of strokes are preventable. Stroke is the second leading cause of death worldwide. To decide which is the best algorithm for stroke prediction, the mechanism exploits the metrics of Accuracy, Precision Jan 1, 2019 · In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. Our study considers stroke diagnosis, (c) stroke treatment, and (d) stroke prog-nostication/outcome prediction. 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 Apr 16, 2023 · The research that is suggested in this paper focuses mostly on different data mining techniques used in heart attack prediction. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. The review sheds light on the state of research on machine learning-based stroke prediction at the moment. The main organ of the human body is the heart. Advancing Stroke Research and Care: The findings and methodologies presented in this study have broader implications for advancing stroke research and care. Review encourages in the development of more robust, efficient, and interpretable predictive models for brain stroke prediction, thereby significantly improving patient outcomes 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. The number of people at risk for stroke 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 Number of people who died from the stroke is less than the 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. been developed for predicting the risk of stroke. In this work, we compare different methods with our approach for stroke 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 Sep 29, 2020 · Study characteristics. 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 Jan 4, 2024 · The outcomes of the proposed approach for stroke prediction in IOT healthcare systems show that improved performance is attained using deep learning methods. Ebenezar*3, CH. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. Capitalizing on the advantages of ML, physicians, and researchers will also be able to predict more accurately which type of interventions will be most effective for which Aug 1, 2017 · This research proposes early prediction of stroke disease using different machine learning approaches such as Logistic Regression Classifier, Decision Tree Classifier, Support Vector Machine and Mar 1, 2022 · This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. Paper analyzes different machine learning methods for stroke prediction. FOR AUTHORS and P. If left untreated, stroke can lead to death. 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 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 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. Methods Retinal vascular parameters were extracted from the UK Biobank fundus images using the Retina-based Microvascular Health Assessment System. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with May 15, 2024 · Problems with data pre-processing and balancing, global data, structured prediction, and insufficient data for training remained unsolved. Similar to this, CT pictures are a common dataset in stroke. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. 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 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. An early intervention and prediction could prevent the occurrence of stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. If a stroke is identified early enough, it is possible to receive the appropriate therapy and recover from the stroke. It's a medical emergency; therefore getting help as soon as possible is critical. Jul 1, 2021 · In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. Stroke causes the unpredictable death and damage to multiple body components. At least, papers from the past decade have been considered for the review. 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. Random Forest showed the highest accuracy of about 96%, due stroke prediction. 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. [8] “Focus on stroke: Predicting and preventing stroke” Michael Regnier- This paper focuses on cutting-edge prevention of stroke. Therefore, it is vital to study the interdependency of these risk factors 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. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. 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 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. 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 learning algorithms. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. The purpose of this study was to analyse and diagnose Jan 5, 2024 · Heart disease and strokes have rapidly increased globally even at juvenile ages. Early … Jan 9, 2025 · Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. This paper proposes an intelligent stroke prediction framework based on a critical examination of machine learning prediction algorithms in the literature. </p In this paper, a machine 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. et al. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Using a mix of clinical variables (age and stroke severity), a process IRE 1703646 ICONIC RESEARCH AND ENGINEERING JOURNALS 273 Brain Stroke Prediction Using Machine Learning Approach DR. The complex 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. The study will utilize various sampling algorithms, such as Random Over Sam- 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. An overlook that monitors stroke prediction. In recent years, some DL algorithms have approached human levels of performance in object recognition . 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. The timely diagnosis of stroke heavily relies on medical imaging techniques such as magnetic Oct 1, 2021 · Request PDF | On Oct 1, 2021, Songhua Liu and others published Paint Transformer: Feed Forward Neural Painting with Stroke Prediction | Find, read and cite all the research you need on ResearchGate A stroke occurs when the blood supply to a person's brain is interrupted or reduced. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. In total, our meta-analysis of ML and cardiovascular diseases included 103 cohorts (55 studies) with a total Jun 22, 2021 · In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with machine learning techniques. paper can be additionally reached out to JETIR2204518 Journal of Emerging Technologies and Innovative Research (JETIR) www. Seeking medical help right away can help prevent brain damage and other complications. Methods We searched PubMed and Web of Science this paper, the authors proposed the model by under-sampling the majority class in the target variable, stroke, and it was reduced up to 498. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. ucc udvjl hfjrs cyzosv ryuyhg chhlww nmhbl pmr bokb ugtq lsrjg kgrqoyn boisw cit wismn