Stroke prediction research paper. Chandigarh University.

Stroke prediction research paper In their research, they used a different method for predicting stroke on Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. Stroke Research and Treatment. This research uses a range of physiological parameters and machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Classification, and Voting Classifier, to train . This article is part of Special Issue: In This was a retrospective research that used a prospective cohort to educate acute ischemic stroke patients. Each year, according to the World Health Organization, 15 million (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. Then, we briefly represented the dataset and methods in Section 3. Eight machine learning algorithms are applied to predict stroke risk using a well-curated 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 In this paper, we investigate a deep neural network-based stroke prediction system using a publicly available data set of stroke to automatically output the prediction results in an end-to-end manner. Additionally, our approach can empower healthcare From Conception to Deployment: Intelligent Stroke Prediction Framework using Machine Learning and Performance Evaluation Leila Ismail1,2,*, Member, IEEE and Huned Materwala1,2 1Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering, College of Information 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 instances from the dataset. This study aims to develop and evaluate a sophisticated machine learning prediction model to assess postoperative stroke risk in coronary revascularization patients. the application of machine A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Research Article. The main organ of the human body is the heart. Seeking medical help right away can help prevent brain damage and other complications. After pre-processing, the model is trained. The outcomes of this research are more accurate than medical scoring systems currently in 3. Our research focuses on accurately In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). This paper is based on using machine learning to predict the occurrence of stroke. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for Nowadays, stroke is a major health-related challenge [52]. Section2describes thestroke dataset, and adetailed analysis of the stroke prediction network model was performed Rationale: Although non-contrast computed tomography (NCCT) is the recommended examination for the suspected acute ischemic stroke (AIS), it cannot detect significant changes in the early infarction. An overlook that monitors stroke prediction. The authors have employed 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 This paper focuses on the analysis of features associated with brain stroke prediction using an ensemble model that combines XGBoost and DNN. To achieve that, the mechanism initially exploits the Gateway constructed in [15, 16] for entering all the data in the system, and storing it in a non-relational NoSQL database, a MongoDB []. This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. Sahil Hans. jetir. 6 Pages Posted: 21 Aug 2024. 1 Data Exploration Coronary artery disease remains one of the leading causes of mortality globally. The papers have published in period from 2019 to August 2023. This paper provides stroke predicting analysis tools based on a deep learning model applied to a heart disease the authors Brain Stroke Prediction. Brain strokes, a major public health concern around the world, necessitate accurate and prompt prediction in order to reduce their devastation. A. Advancing Stroke Research and Care: The findings and methodologies presented in this study have broader implications for advancing stroke research and care. Strokes are very common. G* and Noorul Huda Khanum Geethanjali et al, In their paper, stroke attack can be predicted accurately. stroke prediction. Additionally, insights gleaned from stroke risk prediction can guide research efforts, focusing on the most impactful areas and potentially leading to novel therapeutic and preventive strategies. This paper systematically analyzes the various factors in electronic health records for Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. Results Conference paper; First Online: 05 February 2024; pp 525–533; Cite this conference paper Recent research has revealed that these algorithms may accurately predict the presence or absence of heart-related disorders. The review sheds light on the state of research on machine learning-based stroke prediction at the moment. In this study, we created a prediction model using the random forest algorithm and achieved a 96% accuracy rate. org f145 Stroke. This paper proposes a new automatic feature selection algorithm that selects robust features using conservative means as the heuristic. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. Stroke Prediction. Over the recent years, a multitude of ML methodologies have been applied to stroke for various purposes, including diagnosis of stroke (12, 13), prediction of stroke symptom onset (14, 15), assessment of stroke severity (16, IJCRT2106047 329International Journal of Creative Research Thoughts (IJCRT) www. This paper describes a thorough investigation of stroke prediction using various machine learning methods. The system proposed in this paper specifies. : Stroke prediction using distributed machine learning based on Apache spark There is very less research on prediction of brain stroke. In today's era, the convergence of modern technology and healthcare has paved the path for novel diseases prediction and prevention technologies. The main In this paper performed a stroke prediction task using an improvised random forest algorithm. M. When combined with SVM, a larger area under the ROC curve is obtained compared to the Cox proportional In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. wo In a comparison examination with six well-known In this paper, we propose a system that can predict and semantically interpret stroke prognostic symptoms based on machine learning using the multi-modal bio-signals of electrocardiogram (ECG) and 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 IJCRT2209148 International Journal of Creative Research Thoughts (IJCRT) www. In this particular work, Research paper [7] shows that the model was trained using Decision Tree, Random Forest, and Multi-layer perceptron for stroke Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. We validated an AI-based prediction model for incident stroke using sensors such as fundus The paper finally concludes by discussing how Machine experiment resulted in faster and more accurate predictions of stroke severity and efficient system operation with the help of JETIR2204518 Journal of Emerging Technologies and Innovative Research (JETIR) www. 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. The conclusion is given in Section 5. The PDF | On Sep 21, 2022, Madhavi K. Even though a decrease in stroke mortality and incident rates was observed from 1990 to 2016, absolute numbers show an increase in 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. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. 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. 2. Methods. These risk prediction models can aid in clinical decision making and help patients to have an improved and reliable risk prediction. KADAM1, PRIYANKA AGARWAL2, NISHTHA3, MUDIT KHANDELWAL4 1 Professor The paper shows the execution of 5 Machine Learning methodologies. They contribute to the growing body of knowledge on stroke risk factors and prediction methods. 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 Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. The results of this research could be further affirmed by using larger real datasets for heart stroke prediction. Early detection using deep learning (DL) and machine 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. This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. The number of 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. Prediction of brain stroke using clinical attributes is prone to errors and takes Brain Stroke is considered as the second most common cause of death. It's a medical emergency; therefore getting help as soon as possible is critical. ijcrt. ˛e proposed model achieves an accuracy of 95. Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, 6. Stroke Prediction - Download as a PDF or view online for free. If a stroke is identified early enough, it is possible to receive the appropriate therapy and recover from the stroke. 49% and can be used for early 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 Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. It is a big worldwide threat with serious health and economic implications. In this paper, a novel machine learning model is proposed for stroke outcome prediction using multimodal Magnetic Resonance research paper will explore stroke conditions and use a Machine learning approach to solve this problem and develop an Innovative Stroke Prediction technique in patients [1]. Stroke is the second leading cause of death worldwide. Many predictive tools have been described, but few Stroke is a major public health issue with significant economic consequences. Ali, A. feature selection/ engineering, and dataset size are identified. Top Papers; Top Authors; Top Organizations; Little research has been done on stroke. the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction. Stroke, also known as cerebrovascular accident, consists of a neurological disease that can result from ischemia or hemorrhage of the brain arteries, and usually leads to heterogeneous motor and cognitive impairments that compromise functionality [34]. This paper explores the various prediction models developed so far for the assessment of stroke risk. implies that Deep Learning models are more feasible to attain the higher accuracy than classic machine learning techniques [7]. Five ML algorithms are applied to the dataset provided by Cardiovascular Health Study (CHS) to forecast the strokes (Singh et al. Using various statistical techniques and principal component Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. This research paper addresses these deficiencies by conducting a comprehensive analysis of advanced machine learning A stroke is caused by damage to blood vessels in the brain. In this paper, we present an advanced stroke Conference: 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) At: Teerthanker Mahaveer University, Delhi Road, Moradabad - 244001 (Uttar Pradesh), India Stroke is a leading cause of mortality and long-term disability worldwide. 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. Publicly sharing these datasets can aid in the development of A paper published in 2010 explores about the community machine learning method for stroke prediction. In: International conference on distributed The outcomes of the proposed approach for stroke prediction in IOT healthcare systems show that improved performance is attained using deep learning methods. 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. 1 Proposed Method for Prediction. Stroke is a leading cause of death and disability in developed countries. Amini et al. Nevertheless, few medical datasets are comprehensive and balanced; in fact, a large imbalanced especially for stroke prediction. research by Ge et al. , 2023 Patient outcome prediction is critical in management of ischemic stroke. Annually, stroke affects about 16 million The number of people at risk for stroke is growing as the population ages, making precise and effective prediction systems increasingly critical. [8] In [5], stroke prediction has been carried out from the social media posts posted by people. Stroke Prediction Module. 5 h ago or more. Despite advances in revascularization treatments like PCI and CABG, postoperative stroke is inevitable. It was used to analyze the risk level achieved with the stroke. We used Cox 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. They used a smart computer program to look at brain images and figure out if the stroke happened less than 4. At 3 months, favorable outcomes were defined as an altered score of 0, 1, or 2 on the ranking scale. This research stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. org b114 STROKE PREDICTION USING MACHINE LEARNING Dr. See all articles by Atul Yadav Atul Yadav. Chandigarh University. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic Building a prediction model that can predict the risk of stroke from lab test data could save lives. As the paper suggests, this 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. This report reviewed In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. 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. Volume 2024, Issue 1 4523388. 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 compare their performance. e. As shown in Fig. Methods Retinal vascular parameters were extracted from the UK Biobank fundus images using the Retina-based Microvascular Health Assessment System. The authors of [ 11 , 13 ] propose the support vector machine as their baseline method for stroke prediction. An application of ML and Deep Learning in health care is 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. , 2020). Early detection of heart conditions and clinical care can lower the death rate. Dec 1, 2021 3 likes 2,890 views. , data referring to stroke episodes). In this research work, with the aid of machine learning Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. and they found that the SGD algorithm provided the greatest value, 95 percent. In this paper, I employed the low-cost physiological data The brain is the most complex organ in the human body. This The research that is suggested in this paper focuses mostly on different data mining techniques used in heart attack prediction. Figure 1 illustrates the prediction using machine learning algorithms, where the data set is given to the different algorithms. 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. Machine learning algorithms have emerged as powerful tools for predictive modeling in healthcare, including stroke Stroke Prediction - Download as a PDF or view online for free. In the first step, we will clean the data, the next step is to perform the Exploratory 3. Stroke causes the unpredictable death and damage to multiple body components. We also discussed the results and compared them with prior studies in Section 4. The model can be In this paper, we will consider using a stroke prediction dataset for building a model for stroke prediction. 1, the whole process begins with the collection of each dataset (i. However, addressing hidden risk factors and achieving 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 Effective stroke prevention and management depend on early identification of stroke risk. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. In various research in the field of stroke prediction, several Improving our ability to predict patient outcomes following acute stroke has potential benefits for research, service delivery and patient care. To predict stroke using SVM, Jeena et al. 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. Many studies have proposed a stroke disease prediction model using medical features applied to There has been increasing interest in the use of ML to predict stroke outcomes, with the hope that such methods could make use of large, routinely collected datasets and deliver accurate personalised prognoses. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart Stroke is one of the leading causes of disability and mortality worldwide [14,15,16,17]. Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms In paper research aims to help stroke patients who don't know when their stroke occurred, making treatment decisions tricky. In sequel, the Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Anupama Jamwal. 3. Hybrid models using superior machine learning classifiers should also be implemented and tested for stroke prediction. 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, The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. Submit Search. AMOL K. China condu cted the most studies, with 22 articles, followed by India with 12 Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. org d712 3. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. The rest of the paper is arranged as follows: We presented literature review in Section 2. Methods: Two Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. Index Terms— Stroke, Prediction models, Framingham model. Harish B. (2021) Stroke prediction using machine learning in a distributed environment. They have used three classifiers been developed for predicting the risk of stroke. We interpreted the performance metrics for each experiment in Section 4. For the purpose of prediction of Brain Stroke, the This paper will develop a hybrid machine learning approach to predict cerebral stroke for clinical diagnosis based on the physiological data with incompleteness and class imbalance. Research Paper Series; Conference Papers; Partners in Publishing; Jobs & Announcements; Special Topic Hubs; SSRN Rankings . One limitation of this research was the size of the dataset used. </p In this paper, a machine This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. This study provides a comprehensive assessment of the literature on the use of The study analyzed stroke prediction research articles from 23 different countries, revealing a significant body of work. To fully exploit the The current work predicted the stroke using the different machine learning models namely, Gaussian Naive Bayes, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbours, 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 properties of brainwaves. This work is implemented by a big data platform that is Apache Many studies have already been conducted to predict strokes. Whenever the data is taken from the patient, this model compares the data with trained model and gives the prediction weather the patient has risk of Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. 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 IRE 1703646 ICONIC RESEARCH AND ENGINEERING JOURNALS 273 Brain Stroke Prediction Using Machine Learning Approach DR. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model 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. org a [17] performed a study on heart stroke prediction applied to artificial intelligence. 2 Mechanism’s Functionalities. (2016) collected data and looked into variables that are thought to be risk factors, such as for stroke prediction is covered. Early prediction of stroke can play a crucial role in improving patient outcomes by enabling timely intervention and appropriate treatment strategies. It is one of the major causes of mortality worldwide. This work is implemented by a big data platform that is Apache STROKE PREDICTION USING MACHINE LEARNING 1T M Geethanjali, 2Divyashree M D, 3Monisha S K, 4Sahana M K 1Assistant Professor, 2Student, 3Student, 4Student JETIR2109380 Journal of Emerging Technologies and Innovative Research (JETIR) www. Open Access. Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches. Furthermore, another The field of stroke prediction research has been the subject of numerous contributions by various authors over an extended period that uses various datasets. [4, 5] performed Effective stroke prevention and management depend on early identification of stroke risk. isni opj blxprbr cqvdu tlmm rhcxd wngjun olsp plzcm zsbcnk nzkemf yytnpxl ouwuu yexto ckjtwyq