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Stroke prediction research paper 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 Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Review encourages in the development of more robust, efficient, and interpretable predictive models for brain stroke prediction, thereby significantly improving patient outcomes and reducing the societal burden Proceedings of the International Conference on Inventive Research in Computing Applications (ICIRCA 2022) IEEE Xplore Part Number: CFP22N67-ART; ISBN: 978-1-6654-9707-7 978-1-6654-9707-7/22/$31. a group of academics conducted research on stroke prediction using machine learning models. By comparing the results obtained from various algorithms, researchers can determine which models offer the highest accuracy, precision, recall, or other evaluation metrics. Amol K. Research Drive. 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. They are explained below: In 2014, Hamed Asadi, Richard Dowling, Bernard Yan, Peter Mitchell [1], conducted a look back study on a 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. 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 paper concludes which algorithm is most appropriate for the prediction of stroke. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average . This work is implemented by a big In our research paper, we describe about four machine . Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. Additionally, our approach can empower healthcare Through the synthesis of existing research, this paper identifies trends, best practices, and gaps in current literature, providing valuable insights for our research. Kadam "Brain Stroke Prediction using Machine Learning Approach" Iconic Research And Engineering Journals, 6(1) About IRE Journals IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. This work is implemented by a big data platform that is Apache Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. The stroke prediction dataset was used to perform the study. L. There were 5110 previously published papers related to work on prediction of stroke types using different machine learning approaches. Publicly sharing these datasets can aid in the development of International Journal of Research Publication and Reviews, Vol 3, no 12, pp 711-722, December 2022 International Journal of Research Publication and Reviews Journal homepage: www. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. Results The empirical evaluation yields encouraging results, with the logistic Stroke is a cause of death and long-term disability and requires timely diagnosis and effective preventive treatment. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. Then, we briefly represented the dataset and methods in Section 3. Aim is to The research that is suggested in this paper focuses mostly on different data mining techniques used in heart attack prediction. 7%), highlighting the efficacy of non This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. implies that Deep Learning models are more feasible to attain the higher accuracy than classic machine learning techniques [7]. Several studies have been conducted using the Stroke Prediction Dataset in recent years, and the results have been Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. We interpreted the performance metrics for each experiment in Section 4. An overlook that monitors stroke prediction. However, in this paper, recent contributions are focused that utilize the same dataset as these are also used for evaluation as well. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart Prior recognition of the various stroke warning signs can help minimize the severity of the stroke. Prediction of brain stroke using clinical attributes is prone to errors and takes Stroke prediction and the future of prognosis research. 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. ijrpr. com JETIR2109380 Journal of Emerging Technologies and Innovative Research (JETIR) www. com Brain Stroke Prediction Using Machine Learning Puranjay The application of AI technology in the assessment of stroke risk can achieve favorable results. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for Our results showed that a prediction model can be created using the random forest algorithm and could achieve an accuracy of 0. have built a stroke prediction framework that uses real-time bio-signals and artificial intelligence to detect stroke This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. In this paper, we present an advanced stroke 1. The purpose of this study is to systematically review published papers on stroke prediction using machine learning algorithms and introduce the most efficient machine less than 1000 records. 23 This diversity in data set sizes and types underscores the varied approaches to ML-based stroke prediction in current research. Arvind Choudhary Department of Computer Engineering Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. 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. Sona4, E. This paper systematically analyzes the various factors in electronic health records for predictions and provide correct analysis. 31 To improve the predictive performance in this subset of patients, the CHA Stroke is a major public health issue with significant economic consequences. Abhilash3, K. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022: 20-25. Conclusions. 4. The work done so far on the topic of stroke mainly includes work on heart rate prediction. This Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques January 2023 European Journal of Electrical Engineering and Computer Section 2 examines prior research involved in EEG features in stroke patients as well as computer engineering studies related to stroke prediction. The model predicts the chances a person will have 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 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. The main organ of the human body is the heart. Conference paper; First Online: 05 February 2024; pp 525–533; Cite this Recent research has revealed that these algorithms may accurately predict the presence or absence of (2021) Stroke prediction using machine learning in a distributed environment. 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. JETIR2204518 Journal of Emerging Technologies and Innovative Research (JETIR) www. The aims of this study were to (i) compare Cox and ML models for prediction of risk of stroke in China at varying intervals of follow-up (ie, stroke within 9 years, 0–3 years, 3–6 years, 6–9 years); (ii) identify individuals for whom ML models might be superior to conventional Cox-based approaches for stroke risk prediction; and (iii) develop and evaluate an ensemble model Research Article Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches Chunhua Gao1 and Hui Wang 2 1School of Tourism and Physical Health, Hezhou University, Hezhou 542899, China 2School of Artificial Intelligence, Hezhou University, Hezhou 542899, China Correspondence should be addressed to Hui Wang; syswangxueleng@163. com3, askaviya04@gmail. [9] “Effective Analysis and Predictive Model of Stroke Disease In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. FN False-negative- The patient experiences a stroke, but The brain is the most complex organ in the human body. As shown in Fig. 96. Transforming Stroke Care: The Impact of Artificial Intelligence in Early Detection, Prediction, and Rehabilitation Nithya. In our research paper, we’ve employed cutting-edge classification techniques to predict and mitigate the risk of stroke occurrences. jetir. The conclusion is given in Section 5. 2. Early recognition This research used 1,266 stroke patients from database who had suffered in a transient ischemic attack The Bayesian Rule Lists generated stroke prediction model employing the Market Scan Medicaid Multi-State Database (MDCD) In this paper, we compared three techniques: the deep learning technique; In a new study of 1,102 patients, a multi-item prognostic tool has been developed and validated for use in acute stroke. org d710 STROKE PREDICTION USING MACHINE LEARNING 1T M Geethanjali, 2Divyashree M D, Few researchers worked on Stroke Prediction using Machine Learning. 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. 2 Mechanism’s Functionalities. In ten investigations for stroke issues, Support Vector Machine (SVM) was found to be the best models. The main Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. ITERATURE SURVEY In [4], stroke prediction was made on Cardiovascular Health Study (CHS) dataset using five machine learning techniques. org a [17] performed a study on heart stroke prediction applied to artificial intelligence. 8: Prediction of final lesion in Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. com ISSN 2582-7421 * Corresponding author. [8] “Focus on stroke: Predicting and preventing stroke” Michael Regnier- This paper focuses on cutting-edge prevention of stroke. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. Introduction and Related Works. It is a big worldwide threat with serious health and economic implications. China condu cted the most studies, with 22 articles, followed by India with 12 Heart disease and strokes have rapidly increased globally even at juvenile ages. This paper presents a comprehensive study on the application of machine learning techniques for stroke prediction in computational healthcare. CHADS 2 was limited by its difficulty in accurately evaluating low-risk groups. Mohana Sundaram1, G. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. com Mr. Similar to this, CT pictures are a common dataset in stroke. stroke prediction. Recent advances in machine learning (ML) techniques IRE 1703646 ICONIC RESEARCH AND ENGINEERING JOURNALS 273 Brain Stroke Prediction Using Machine Learning Approach DR. Authors Terence J Quinn 1 , Bogna A Drozdowska 2 Affiliations 1 In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. 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 []. R1, SuruthiSenthilkumar2, Vishnupriya kalyanasundaram3, Kaviya Annamala4, Tarunika Yogaraj5 {nithyar7340@psgpharma. At least, papers from the past decade have been considered for the review. ijcrt. It is one of the major causes of mortality worldwide. 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 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 This paper explores a machine learning approach to stroke prediction. Previous research showed that AI algorithms can be used for early diagnosis of atrial fibrillation using normal sinus rhythm 3. In recent years some of them are described below. Advancing Stroke Research and Care: The findings and methodologies presented in this study have broader implications for advancing stroke research and care. Neha Saxena Department of Computer Engineering Universal College of Engineering, Vasai, India nehasaxena031@gmail. Strokes are very common. 0% accuracy in predicting stroke, with low FPR (6. PDF | On May 19, 2024, Viswapriya Subramaniyam Elangovan and others published Analysing an imbalanced stroke prediction dataset using machine learning techniques RESEARCH PAPER. This paper proposes a new automatic feature selection algorithm that selects robust features using conservative means as the heuristic. Our research focuses on accurately Research Paper Detection of Brain Stroke Using Machine Learning Algorithm K. FP False-positive- the patient did not have a stroke, yet the test returns a positive result. As an optimal solution, the authors used a combination of the Decision Tree with the C4. The system proposed in this paper specifies. Divya sri5, C. doi: 10. Many studies have proposed a stroke disease The field of stroke prediction research has been the subject of numerous contributions by various authors over an extended period that uses various datasets. 11. Machine learning applications are becoming more widely used in the health care Automated Stroke Prediction Using Machine Learning: An Explainable and Exploratory Study With a Web Application for Early Intervention January 2023 IEEE Access PP(99):1-1 In this paper, we will consider using a stroke prediction dataset for building a model for stroke prediction. ac. In their research, they used a different method for predicting stroke on Priyanka Agarwal , Mudit Khandelwal , Nishtha , Dr. com2, vishnupriyakpharma@gmail. The stroke deprives a person’s brain of 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 A paper published in 2010 explores about the community machine learning method for stroke prediction. 3. Section 3 explores deep Request PDF | Stroke prediction using artificial intelligence | A stroke occurs when the blood supply to a person’s brain is interrupted or reduced. Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Enhanced Hierarchal Clustering is applied on the dataset, then five classifiers Stroke prediction demands accurate identification of individuals in the early stages of the disease, as it is crucial for effective treatment. especially for stroke prediction. In: International conference on distributed computing and internet AI holds significant potential in heart stroke prediction and diagnosis; however, it must confront parallel challenges to ensure precision and interpretability in its application by healthcare professionals. These might be thought of as two sides of the same coin. 3. In this paper, I employed the low-cost physiological data, which has been overlooked in Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. 04%, and the random forest and neural network models scoring A stroke is caused when blood flow to a part of the brain is stopped abruptly. 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, A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Stroke instances from the dataset. Little research has been done on stroke. in1, sksuruthi21@gmail. com4, Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic Section 2 examines prior research involved in EEG features in stroke patients as well as computer engineering studies related to stroke prediction. Prediction of stroke is a time consuming and tedious for doctors. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction. Early detection of heart conditions and clinical care can lower the death rate. The objective of this research is to develop a robust and accurate stroke prediction model that can assist healthcare professionals in identifying Prediction of Stroke using Data Mining Classification Techniques Ohoud Almadani, Master of Health Informatics (MHI), and Registered Pharmacist (R. TN True negative- the patient hasn't had a stroke and the test has come back negative. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. KADAM1, PRIYANKA AGARWAL2, NISHTHA3, MUDIT KHANDELWAL4 1 Professor The paper shows the execution of 5 Machine Learning methodologies. 5 algorithm, This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. 1038/s41582-019-0181-5. 1, the whole process begins with the collection of each dataset (i. Stroke prediction and the future of prognosis research Nat Rev Neurol. Stacking, a sophisticated ensemble This paper describes a thorough investigation of stroke prediction using various machine learning methods. Prior work aiming to characterise ischaemic stroke risk in AF patients has focused on clinical scores, such as CHADS 2, CHA 2 DS 2-VASc and ATRIA. 32628/CSEIT2283121. Contemporary lifestyle factors, including high glucose etc. We also discussed the results and compared them with prior studies in Section 4. E-mail address: puranjaysavarmattas@gmail. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, A stroke is caused by damage to blood vessels in the brain. The number of promising results in various medical domains. The rest of the paper is arranged as follows: We presented literature review in Section 2. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. This paper is based on using machine learning to predict the occurrence of stroke. Brain stroke has been the subject of very few studies. Stroke, characterized by a sudden interruption of blood flow to the brain, discourse in stroke prediction research is enriched by the synthesis of insights from previous studies and the novel deployment on an interactive platform. [2]. Haritha2, A. The study concludes that optimizer a stroke clustering and prediction system called Stroke MD. They contribute to the growing body of knowledge on stroke risk factors and prediction methods. Ph) Pharmacutical care department at King Abdulaziz Medical City Riyadh, KSA Riyad Alshammari King Saud bin Abdulaziz University for Health Sciences King Abdullah International Medical Research Center research by Ge et al. D. Because of the role they play in the Fourth Industrial Revolution, artificial intelligence, big data, the Internet of Stroke Risk Prediction Using Machine Learning Algorithms. , data referring to stroke episodes). learning classification algorithms (KNN, the proposed approach demonstrates superior stroke prediction accuracy compared to individual 2019. II. AMOL K. org f143 BrainOK: Brain Stroke Prediction using Machine Learning Mrs. Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate 6. 0%) and FNR (5. ‘s study 41 reveals that the LSTM model applied to raw EEG data achieved a 94. Request PDF | On Dec 1, 2016, R S Jeena and others published Stroke prediction using SVM | Find, read and cite all the research you need on ResearchGate Choi et al. The research methodology included (1) dataset TP True positive-means that the patient has had a stroke and the test has come back positive. The 4 th Industrial Revolution has arrived, bringing with it a wide range of businesses and research fields and huge opportunities as well as substantial challenges. 2019 Jun;15(6):311-312. Classifier and Rules • This model is rule-based and allows to generate rules automatically or to define custom rules according to data; the model can handle missing In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). Seeking medical help right away can help prevent brain damage and other complications. In the proposed model, heart stroke prediction is performed on a dataset collected from Kaggle. Using a mix of clinical variables (age and stroke severity), a process The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. Both machine learning (Random Forest) and deep The study analyzed stroke prediction research articles from 23 different countries, revealing a significant body of work. In this paper, Section 3 demonstrates the research methodology which includes Sirsat et al. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Using various statistical techniques and principal component 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. 1. This paper describes a thorough investigation of stroke prediction using various machine learning methods. It's a medical emergency; therefore getting help as soon as possible is critical. Different machine learning (ML) models have been developed to The comprehensive analysis of various advanced machine learning models for stroke prediction that are presented in this research paper sheds light on the efficacy of The current work predicted the stroke using the different machine learning models namely, Gaussian Naive Bayes, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbours, Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. 00 Clinical Stroke Risk Assessment in Atrial Fibrillation. In this research, recent studies that proposed stroke prediction frameworks using data mining approaches have been reviewed, and a new hybrid framework is proposed to predict stroke disease using two main steps, clustering and classification. 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 significant challenge for machine IJCRT2106047 329International Journal of Creative Research Thoughts (IJCRT) www. Stroke is the second leading cause of death worldwide. e. 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. This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. Figure 3 clearly illustrates a substantial and rapid increase in the number of papers related to brain stroke research from 2018 to 2022. 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. tsebq xvhjm uxto bosoxz fviyo rpun nviawq erfvyv lwdb fdhjr dgxt pomwkkw pjtp pwoqqjxo nhil
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