Stroke prediction research paper. 2 Mechanism’s Functionalities.

Stroke prediction research paper They contribute to the growing body of knowledge on stroke risk factors and prediction methods. 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. Previous research showed that AI algorithms can be used for early diagnosis of atrial fibrillation using normal sinus rhythm 3. 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. 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. The rest of the paper is arranged as follows: We presented literature review in Section 2. 96. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022: 20-25. The main organ of the human body is the heart. In this paper, we present an advanced stroke 1. Aim is to The research that is suggested in this paper focuses mostly on different data mining techniques used in heart attack prediction. 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. JETIR2204518 Journal of Emerging Technologies and Innovative Research (JETIR) www. e. Early detection of heart conditions and clinical care can lower the death rate. 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. 1038/s41582-019-0181-5. [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. PDF | On May 19, 2024, Viswapriya Subramaniyam Elangovan and others published Analysing an imbalanced stroke prediction dataset using machine learning techniques RESEARCH PAPER. ITERATURE SURVEY In [4], stroke prediction was made on Cardiovascular Health Study (CHS) dataset using five machine learning techniques. Sona4, E. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. 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. org a [17] performed a study on heart stroke prediction applied to artificial intelligence. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. com2, vishnupriyakpharma@gmail. 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. 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. Then, we briefly represented the dataset and methods in Section 3. org f143 BrainOK: Brain Stroke Prediction using Machine Learning Mrs. 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. L. a group of academics conducted research on stroke prediction using machine learning models. 1, the whole process begins with the collection of each dataset (i. [2]. It is one of the major causes of mortality worldwide. Introduction and Related Works. 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. Prediction of brain stroke using clinical attributes is prone to errors and takes Stroke prediction and the future of prognosis research. 3. 1. ijrpr. 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. 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. 11. 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. In the proposed model, heart stroke prediction is performed on a dataset collected from Kaggle. 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. Transforming Stroke Care: The Impact of Artificial Intelligence in Early Detection, Prediction, and Rehabilitation Nithya. 0% accuracy in predicting stroke, with low FPR (6. 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. 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. , data referring to stroke episodes). 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. E-mail address: puranjaysavarmattas@gmail. In recent years some of them are described below. implies that Deep Learning models are more feasible to attain the higher accuracy than classic machine learning techniques [7]. This work is implemented by a big In our research paper, we describe about four machine . ijcrt. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. In this paper, Section 3 demonstrates the research methodology which includes Sirsat et al. com3, askaviya04@gmail. 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. Conclusions. Strokes are very common. 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. 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. 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. Our research focuses on accurately Research Paper Detection of Brain Stroke Using Machine Learning Algorithm K. 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. 31 To improve the predictive performance in this subset of patients, the CHA Stroke is a major public health issue with significant economic consequences. 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. At least, papers from the past decade have been considered for the review. 32628/CSEIT2283121. Amol K. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. The study concludes that optimizer a stroke clustering and prediction system called Stroke MD. R1, SuruthiSenthilkumar2, Vishnupriya kalyanasundaram3, Kaviya Annamala4, Tarunika Yogaraj5 {nithyar7340@psgpharma. 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). FP False-positive- the patient did not have a stroke, yet the test returns a positive result. This paper describes a thorough investigation of stroke prediction using various machine learning methods. This paper is based on using machine learning to predict the occurrence of stroke. com Mr. 2019 Jun;15(6):311-312. com Brain Stroke Prediction Using Machine Learning Puranjay The application of AI technology in the assessment of stroke risk can achieve favorable results. 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. ac. There were 5110 previously published papers related to work on prediction of stroke types using different machine learning approaches. doi: 10. 5 algorithm, This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. 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. 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. Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. 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. Both machine learning (Random Forest) and deep The study analyzed stroke prediction research articles from 23 different countries, revealing a significant body of work. We also discussed the results and compared them with prior studies in Section 4. In our research paper, we’ve employed cutting-edge classification techniques to predict and mitigate the risk of stroke occurrences. In their research, they used a different method for predicting stroke on Priyanka Agarwal , Mudit Khandelwal , Nishtha , Dr. Prediction of stroke is a time consuming and tedious for doctors. Mohana Sundaram1, G. Abhilash3, K. 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. The research methodology included (1) dataset TP True positive-means that the patient has had a stroke and the test has come back positive. This paper presents a comprehensive study on the application of machine learning techniques for stroke prediction in computational healthcare. Figure 3 clearly illustrates a substantial and rapid increase in the number of papers related to brain stroke research from 2018 to 2022. 00 Clinical Stroke Risk Assessment in Atrial Fibrillation. CHADS 2 was limited by its difficulty in accurately evaluating low-risk groups. Research Drive. learning classification algorithms (KNN, the proposed approach demonstrates superior stroke prediction accuracy compared to individual 2019. Similar to this, CT pictures are a common dataset in stroke. Recent advances in machine learning (ML) techniques IRE 1703646 ICONIC RESEARCH AND ENGINEERING JOURNALS 273 Brain Stroke Prediction Using Machine Learning Approach DR. 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. Stroke instances from the dataset. As shown in Fig. Divya sri5, C. 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 . AMOL K. 23 This diversity in data set sizes and types underscores the varied approaches to ML-based stroke prediction in current research. 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. It's a medical emergency; therefore getting help as soon as possible is critical. Stroke is the second leading cause of death worldwide. Brain stroke has been the subject of very few studies. 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. Little research has been done on stroke. Seeking medical help right away can help prevent brain damage and other complications. in1, sksuruthi21@gmail. 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. ‘s study 41 reveals that the LSTM model applied to raw EEG data achieved a 94. Contemporary lifestyle factors, including high glucose etc. 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. 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. Stroke prediction and the future of prognosis research Nat Rev Neurol. Neha Saxena Department of Computer Engineering Universal College of Engineering, Vasai, India nehasaxena031@gmail. An overlook that monitors stroke prediction. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. 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. KADAM1, PRIYANKA AGARWAL2, NISHTHA3, MUDIT KHANDELWAL4 1 Professor The paper shows the execution of 5 Machine Learning methodologies. This paper proposes a new automatic feature selection algorithm that selects robust features using conservative means as the heuristic. 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. 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. 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. The main Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. [8] “Focus on stroke: Predicting and preventing stroke” Michael Regnier- This paper focuses on cutting-edge prevention of stroke. 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. It is a big worldwide threat with serious health and economic implications. II. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. 0%) and FNR (5. 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 disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. 4. 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. We interpreted the performance metrics for each experiment in Section 4. com JETIR2109380 Journal of Emerging Technologies and Innovative Research (JETIR) www. stroke prediction. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. 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. 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. 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. The system proposed in this paper specifies. As an optimal solution, the authors used a combination of the Decision Tree with the C4. org d710 STROKE PREDICTION USING MACHINE LEARNING 1T M Geethanjali, 2Divyashree M D, Few researchers worked on Stroke Prediction using Machine Learning. com ISSN 2582-7421 * Corresponding author. 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. 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. This paper systematically analyzes the various factors in electronic health records for predictions and provide correct analysis. 2. jetir. especially for stroke 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. 2 Mechanism’s Functionalities. 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. 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. The stroke prediction dataset was used to perform the study. The number of promising results in various medical domains. Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate 6. 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. Stacking, a sophisticated ensemble This paper describes a thorough investigation of stroke prediction using various machine learning methods. Haritha2, A. 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 conclusion is given in Section 5. TN True negative- the patient hasn't had a stroke and the test has come back negative. These might be thought of as two sides of the same coin. 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. However, in this paper, recent contributions are focused that utilize the same dataset as these are also used for evaluation as well. 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. 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 []. By comparing the results obtained from various algorithms, researchers can determine which models offer the highest accuracy, precision, recall, or other evaluation metrics. The work done so far on the topic of stroke mainly includes work on heart rate prediction. 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. 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. Advancing Stroke Research and Care: The findings and methodologies presented in this study have broader implications for advancing stroke research and care. In ten investigations for stroke issues, Support Vector Machine (SVM) was found to be the best models. 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. 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. FN False-negative- The patient experiences a stroke, but The brain is the most complex organ in the human body. 3. D. lvxpfuvx iye gvwh ufzsnx vzrss qwwj kpoktz ddtmm arbmvwb sks xeiwf forh nzo awgc kol