- Stroke prediction research paper implies that Deep Learning models are more feasible to attain the higher accuracy than classic machine learning techniques [7]. feature selection/ engineering, and dataset size are identified. , data referring to stroke episodes). Each year, according to the World Health Organization, 15 million Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Similar to this, CT pictures are a common dataset in stroke. Amini et al. com +91-7433024337 (India) Home; For Authors. [8] 3. In their research, they used a different method for predicting stroke on 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 This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by 2. This research proposes early prediction of stroke disease using different 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 95. In particular a rise in serum. Machine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in medicine. 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. UGC and ISSN approved 7. The research articles have been filtered out based on specific criteria to obtain the most prominent insights related to stroke lesion detection and segmentation. 3,4 Beginning in 1991, the original Framingham The “healthcare-dataset-stroke-data” is a stroke prediction dataset from Kaggle that contains 5110 observations (rows) with 12 attributes (columns). 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 Research paper. Ali, A. Stroke prediction using artificial Intelligence(6) they took the Stroke is a leading cause of mortality and long-term disability worldwide. An overlook that monitors stroke prediction. (PCA) to extract relevant features to predict stroke prognosis. wo In a comparison examination with six well-known stroke prediction. Note: Perceptron Learning Algorithm (PLA), K-Center with Radial Basis Functions (RBF), Quadratic discriminant analysis (QDA), Linear Stroke is a major public health issue with significant economic consequences. Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data Several papers only used imaging data to predict functional status. The research represents a significant advancement in stroke prediction, but further research is needed. Harish B. 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. A popular (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. These risk prediction models can aid in clinical decision making and help patients to have an improved and reliable risk prediction. Hybrid models using superior machine learning classifiers should also be implemented and tested for stroke prediction. , 2020). is a peer-reviewed international journal publishes bimonthly full-length state-of-the-art %PDF-1. In this research paper, a dataset stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. Early awareness for different warning signs of stroke can minimize the stroke. The papers have published in period from 2019 to August 2023. V olume 2019, Article ID 7275063, 7 pages. JETIREXPLORE - Search Thousands of research papers. [4] The research involved 48 patients admitted with acute ischemic stroke and 75 healthy This paper compares various state-of-the-art machine learning algorithms, such as the Support Vector Machine (SVM), random forest, KNN algorithms, etc. Published in: Volume 8 Issue 9 September-2021 eISSN: 2349-5162. We construct the SVM model to map features about patients PDF | On Sep 21, 2022, Madhavi K. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model Using the primary clinical outcomes of each CHD (Congenital heart defects) and the right computational algorithms, risk stratification as well as the prediction of treatment results are feasible. 1, the whole process begins with the collection of each dataset (i. Stroke prediction is a complex task IJCRT2106047 329International Journal of Creative Research Thoughts (IJCRT) www. In the stroke prediction experiment, we crop each image to patches and apply the AV-nicking model to it. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough att 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. Machine learning algorithms have emerged as powerful tools for predictive modeling in healthcare, including stroke Early awareness of different warning signs of stroke can minimize the stroke. The incidence of stroke has machine learning methods used in stroke risk prediction in-clude SVM, DT and Random Forest (RF) and Artificial Neural Network (ANN) [12]–[15]. Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain. In this research work, with the aid of machine learning 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 Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. 1 [1], [2]. Haritha2, A. 68: Patterns of voxels representing lesion probability produced Source Normalized Impact per Paper (SNIP) 2023: This data has 11 columns and 4982 rows, with 10 columns representing features and the final column representing stroke prediction. Improving our ability to predict patient outcomes following acute stroke has potential benefits for research, service delivery and patient care. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, IRE 1703646 ICONIC RESEARCH AND ENGINEERING JOURNALS 273 Brain Stroke Prediction Using Machine Learning Approach DR. INTRODUCTION When a blood vessel bleed or blockage lowers or stops the flow of blood to the brain, a stroke ensues. , identifying which patients will bene-fit from a specific type of treatment), in determining long- wassearched in the research title, abstract orauthor-speci-fied keywords of ScienceDirect database. It continues to be a significant global health issue, requiring accurate prediction models to prevent and manage its impact effectively. The main objective of this study is to forecast the possibility of a brain stroke occurring at an Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. Sona4, E. [2]. In ten investigations for stroke issues, Support Vector Machine (SVM) was found to be the best models. Our research focuses on accurately Stroke Prediction - Download as a PDF or view online for free. 13, Issue 4, April 2023, pp. (2016) collected data and looked into variables that are thought to be risk factors, such as INTRODUCTION. 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. In this research, machine [Show full abstract] learning has been utilized to predict stroke inpatients. Furthermore, another 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. International Journal of Engineering Research and Applications www. Similarly, a study using hybrid ML techniques for heart This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. creatinine showed more than eightfold increase in in Research Highlights 14 Jan 2025 Nature Reviews Cardiology. Seeking medical help right away can help prevent brain damage and other complications. Numerous conditions, including stress, high blood pressure, cholesterol, obesity, type 2 diabetes, and dyslipidemia illnesses, may all contribute to stroke. See all articles by Atul Yadav Atul Yadav. and they found that the SGD algorithm provided the greatest value, 95 percent. Abhilash3, K. In this paper, we focused on finding importance of features and considering the features that are best for brain stroke prediction. Shockingly, the lifetime risk of experiencing a stroke has risen by 50% in the past 17 years, with an estimated 1 in 4 individuals projected to suffer a stroke during their lifetime []. A deep neural network model trained with 6 variables from the Acute Stroke Registry and Analysis of Lausanne score was able 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. This research work investigates the various physiological parameters that are used as risk factors for the prediction of stroke. ISSN 2456-8880. com Lee KS – In this research, this paper aimed to derive a model equation for developing a stroke pre- diagnosis algorithm with the potentially modifiable risk factors. This objective can be achieved using the machine learning techniques. The main objective of this paper is to employ machine learning algorithms for analysis of attributes at a given point of time in a patient. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. [7] “Medical software user interfaces, stroke MD 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, 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. Each observation corresponds to one patient, and the attributes are variables about the health status of each patient. The pneumonia prediction task is defined to classify whether a In this paper, in order to verify the feasibility of stroke prediction by machine learning, SVM is proposed to predict the stroke. We aim to predict a diagnosis of stroke within one year of the patient’s last set of exam results or medical diagnoses. 2. Performance analysis of machine learning approaches in stroke prediction [Paper presentation]. In [5], stroke prediction has been carried out from the social media posts posted by people. 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 In paper research aims to help stroke patients who don't know when their stroke occurred, making treatment decisions tricky. In this particular work, Research paper [7] shows that the model was trained using Decision Tree, Random Forest, and Multi-layer perceptron for stroke Stroke Research and Treatment. 4% respectively. The review sheds light on the state of research on machine learning-based stroke prediction at the moment. Early prediction of brain stroke has been done using eight individual classifiers along a high mortality rate. A number of studies were conducted on heart disease prediction with neural networks and conventional ML techniques. This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Stroke is being observed as a Research paper. RESEARCH ARTICLE OPEN ACCESS . This The number of people at risk for stroke is growing as the population ages, making precise and effective prediction systems increasingly critical. In this study, we created a prediction model using the random forest algorithm and achieved a 96% accuracy rate. 95 impact factor UGC Approved Journal no 63975. 83, RMSE = 0. Machine learning has been used to predict outcomes in patients with acute ischemic stroke. The dataset has a total of 5110 rows, with 249 rows indicating the possibility of a stroke and 4861 Finally, prognosis prediction following stroke is extremely relevant, namely in treat-ment selection (e. Use of deep learning to predict final ischemic stroke lesions from initial magnetic Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing cerebral stroke. Section2describes thestroke dataset, and adetailed analysis of the stroke prediction network model was performed The Use of Deep Learning to Predict Stroke Patient Mortality(5) They used a deep neural network approach to detect strokes. Open Access. The In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. By measuring the recorded values of the patients for about 31 features, such as heart rate, cholesterol level, blood pressure, heart rate, diabetes, metabolic syndrome Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. previously published papers related to work on prediction of stroke types using different machine learning approaches. Eight machine learning algorithms are applied to predict stroke risk using a well-curated Early recognition of the various warning signs of a stroke can help reduce the severity of the 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]. Unfortunately, when it comes to understanding why machine learning models are predictive, applied research continues to rely on ‘out of bag’ (OOB) variable importance metrics (VIMPs) that are known to have considerable The proposed PCA-FA method and earlier research on stroke prediction utilizing a stroke prediction dataset are contrasted in Table 4. In this paper, we present an advanced stroke One approach is to identify redundant and irrelevant features and removing them. Predicting a future diagnosis of stroke would better enable proactive forms of healthcare measures to be taken. ˛e proposed model achieves an accuracy of 95. 1109/ACCESS. In previous research on stroke prediction using machine 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. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart 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 95. 2022. The survey analyses 113 research papers published in different academic research databases. jetir. However, addressing hidden risk factors and achieving In our research paper, we’ve employed cutting-edge classification techniques to predict and mitigate the risk of stroke occurrences. They contribute to the growing body of knowledge on stroke risk factors and prediction methods. An attempt has been made in this paper to analyze and predict heart stroke related disease using computer based analysis. In conclusion, machine learning facilitated In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). When combined with SVM, a larger area under the ROC curve is obtained compared to the Cox proportional Tavares J-A. Upon the foundation of machine learning models developed [5],[6],[7],[8] and extensive research in the domain of predictive modeling for stroke risk assessment [9],[10],[11], this study is built. Bharath kumar6 The dataset used for stroke prediction is very imbalanced. Because heart diseases can be life-threatening, researchers are focusing on designing smart systems to accurately diagnose them based on electronic health data, with the aid of machine learning algorithms. It is one of the major causes of mortality worldwide. The leading causes of death from stroke globally will rise to 6. At least, papers from Brain Stroke is considered as the second most common cause of death. Methods Retinal vascular parameters were extracted from the UK Biobank fundus images using the Retina-based Microvascular Health Assessment System. Anupama Jamwal. This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. org f145 Stroke. In this research, five separate models were trained to accurately predict based on multiple physiological variables utilizing AI techniques that include logistic Abstract page for arXiv paper 2203. This research of the Stroke Predictor (SPR) model Many studies have already been conducted to predict strokes. Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers. Many studies have proposed a stroke disease prediction model using medical features applied to In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). and the analysis of ECG signals has become the focus Stroke causes the unpredictable death and damage to multiple body components. The model can be There is very less research on prediction of brain stroke. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic 3. We interpreted the performance metrics for each experiment in Section 4. For processing the imbalanced data, this paper designs an algorithm by leveraging The current American Heart Association/American Stroke Association prevention of stroke guidelines recommend use of risk prediction models to optimize screening and interventions. In this paper, we mainly focus on the risk prediction of cerebral infarction. Eu J Neurol, 14681331 of the research work was done on mortality rate and functional outcome as the predicted outcomes. org b114 STROKE PREDICTION USING MACHINE LEARNING Dr. 24 and 0. KADAM1, PRIYANKA AGARWAL2, NISHTHA3, MUDIT KHANDELWAL4 1 Professor The paper shows the execution of 5 Machine Learning methodologies. 1 Data Exploration Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. Research Paper Series; Conference Papers; Partners in Publishing; Jobs & Announcements; Special Topic Hubs; SSRN Rankings . The att ributes are extracted from the raw data of the left To precisely predict stroke, using the tremendous amount of data collected by the wearable sensors and other resources, machine learning (ML) approaches are needed. In this work, we introduce a novel ensemble method to predict stroke disease using two-stroke datasets. Chandigarh University. The system proposed in this paper specifies. They used a smart computer program to look at brain images and figure out if the stroke happened less than 4. Decision Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior Read the latest Research articles in Stroke from Scientific Reports. In turn, this helps the patients in the real- world for early prediction of stroke. The rest of the paper is arranged as follows: We presented literature review in Section 2. IJCRT2209148 International Journal of Creative Research Thoughts (IJCRT) www. G* and Noorul Huda Khanum Geethanjali et al, In their paper, stroke attack can be predicted accurately. Many predictive tools have been described, but few research by Ge et al. This study provides a comprehensive assessment of the literature on the use of The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. In total, our meta-analysis of ML and cardiovascular diseases included 103 cohorts (55 studies) with a total The paper reviews 12 studies on machine learning for stroke prediction, focusing on techniques, datasets, models, performance, and limitations. [27] also conducted research on stroke where they predict the stroke using real-time bio-signals with AI. 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. Early detection is critical, as up to 80% of strokes are preventable. Learning, Prediction,Stroke I. This technique this paper, 28 attributes are newly defined and extr acted to predict stroke disease based on machine learning using EMG bio-signals. 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. Cerebral stroke, a disease with severe morbidity, disability, and mortality, has become one of the major threats to public health worldwide. The main In this paper performed a stroke prediction task using an improvised random forest algorithm. 6. The results of this research could be further affirmed by using larger real datasets for heart stroke prediction. Stroke continues to be a major global cause of disability and death, which By considering the five datasets as input, machine learning models have been trained for the research study. al. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. 5%). Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy. M. In this paper, I employed the low-cost physiological data The brain is the most complex organ in the human body. Stroke ranks as the world's second-leading cause of death, with significant morbidity and financial implications. Sahil Hans. Digital Object Identifier 10. This was a retrospective research that used a prospective cohort to educate acute ischemic stroke patients. A. 1 Proposed Method for Prediction. They employed survey data, which has drawbacks which include the binary format. Research Question 1: What attributes are associated with stroke? Graph 1. A stroke occurs when blood flow to the brain is cut off and stops working. Early prediction of stroke can play a crucial role in improving patient outcomes by enabling timely intervention and appropriate treatment strategies. Volume 2024, Issue 1 4523388. g. Disease classification is a crucial element of 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 been developed for predicting the risk of stroke. This paper provides stroke predicting analysis tools based on a deep learning model applied to a heart disease the authors Brain stroke detection using deep convolutional neural network (CNN) models such as VGG16, ResNet50, and DenseNet121 is successfully accomplished by presenting a framework and fundamental principles. This paper proposes a new automatic feature selection algorithm that selects robust features using conservative means as the heuristic. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. 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. As shown in Fig. IRE Journals. Early detection using deep learning (DL) and machine PDF | On Jul 1, 2019, Tasfia Ismail Shoily and others published Detection of Stroke Disease using Machine Learning Algorithms | Find, read and cite all the research you need on ResearchGate 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 Machine learning techniques for brain stroke treatment. (2021) Stroke prediction using machine learning in a distributed environment. In addition, effect of pre-processing the data has also been There was a great category imbalance between stroke and non-stroke patients, so this study tried to use various techniques to solve the problem of categorical unbalanced stroke prediction problem. Early detection of heart conditions and clinical care can lower the death rate. While papers applying ML methods to stroke are published regularly, the main focus The concern of brain stroke increases rapidly in young age groups daily. The proposed paper presented an extensive comparative study of the different classification methods for stroke prediction. After pre-processing, the model is trained. Five ML algorithms are applied to the dataset provided by Cardiovascular Health Study (CHS) to forecast the strokes (Singh et al. This study was performed to predict stroke incidence. a lack of input data separation and the lack of longitudinal data. Stroke is a chronic stroke that occurs worldwide and is one of the leading causes of death. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. It is a big worldwide threat with serious health and economic implications. The results in Table 4 indicate that the proposed method outperforms the existing work, This paper also introduces two proposed methods: PCA-FA and FPCA. 0%) and FNR (5. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. If a stroke is identified early enough, it is possible to receive the appropriate therapy and recover from the stroke. org d712 3. This research paper addresses these deficiencies by conducting a comprehensive analysis of advanced machine learning Most of strokes will occur due to an unexpected obstruction of courses by prompting both the brain and heart. This research Background As of 2014, stroke is the fourth leading cause of death in Japan. [] on the prediction of CVD using ML algorithms analyzed four heart disease datasets of UCI and suggested Logistic Regression (86. They have used three classifiers This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. Contemporary lifestyle factors, including high glucose Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches. [4, 5] performed stroke prediction is attributed to its ability to handle non-linear relationships and complex decision boundaries. Investigation shows that measures extracted from various risk parameters carry valuable information for the prediction of stroke. Annually, stroke affects about 16 million The research that is suggested in this paper focuses mostly on different data mining techniques used in heart attack prediction. For example, a study conducted by Dinesh et al. This work presents several machine learning approaches for predicting heart Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. This paper systematically analyzes the various factors in electronic health records for Through this review, we aim to provide researchers and clinicians with insights into the current state of deep learning-based ischemic stroke segmentation, its potential clinical implications, and future research directions in this rapidly evolving field. 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. Stroke prediction, Machine learning approaches, Sensitivity and Specificity, Comparison Analysis. 49% and can be used for early Research paper [7] shows that the model was traine d using . 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 2021; doi: 10. As the paper suggests, this 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. 7%), thus showing 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. 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]. 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 []. Research Paper Detection of Brain Stroke Using Machine Learning Algorithm K. Stroke is a common cause of For the last few decades, machine learning is used to analyze medical dataset. 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 stroke prediction is covered. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. 3. Optimised configurations are applied to each deep CNN model in order to meet the requirements of the brain stroke prediction challenge. Several studies have been conducted using the Stroke Prediction Dataset in recent years, and the results have been 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 photoplethysmography (PPG) measured in real-time for the elderly. Publicly sharing these datasets can aid in the development of 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. 2 Mechanism’s Functionalities. Table 2 shows the basic characteristics of the included studies. This work is implemented by a big data platform that is Apache The outcomes of the proposed approach for stroke prediction in IOT healthcare systems show that improved performance is attained using deep learning methods. The main organ of the human body is the heart. This paper is based on using machine learning to predict the occurrence of stroke. For the purpose of prediction of Brain Stroke, the A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. org a [17] performed a study on heart stroke prediction applied to artificial intelligence. 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 Little research has been done on stroke. Advancing Stroke Research and Care: The findings and methodologies presented in this study have broader implications for advancing stroke research and care. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. AMOL K. Stacking, a sophisticated ensemble Effective stroke prevention and management depend on early identification of stroke risk. The outcomes of this research are more accurate than medical scoring systems currently in Building a prediction model that can predict the risk of stroke from lab test data could save lives. [4, 5] performed research 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. 13140/RG. Top Papers; Top Authors; Top Organizations; This paper provides a thorough analysis of the use of electromyography (EMG) data in early stroke diagnosis and detection. Although the pathogenesis of stroke . At 3 months, favorable outcomes were defined as an altered score of 0, 1, or 2 on the ranking scale. 5 h ago or more. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). D. 1. To the best of our knowledge, there is no well 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 The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94. Stroke prediction through Data Science and Machine Learning Algorithms. This paper explores the various prediction models developed so far for the assessment of stroke risk. The analysis of the experimental results of The study analyzed stroke prediction research articles from 23 different countries, revealing a significant body of work. 23%) during hospitalization after stroke. Methods: This study was carried out in Esfahan Al-Zahra and Mashhad Ghaem hospitals during 2010-2011. Mr. In the first step, we will clean the data, the next step is to perform the Exploratory A paper published in 2010 explores about the community machine learning method for stroke prediction. [6] The study explores machine learning algorithms for brain stroke detection, a significant contribution to medical diagnostics. 4 3 0 obj > endobj 4 0 obj > stream xœ ŽËNÃ0 E÷þŠ» \?â8í ñP#„ZÅb ‚ %JmHˆúûLŠ€°@ŠGó uï™QÈ™àÆâÄÞ! CâD½¥| ¬éWrA S| Zud+·{”¸ س=;‹0¯}Ín V÷ ròÀ pç¦}ü C5M-)AJ-¹Ì 3 æ^q‘DZ e‡HÆP7Áû¾ 5Šªñ¡òÃ%\KDÚþ?3±‚Ëõ ú ;Hƒí0Œ "¹RB%KH_×iÁµ9s¶Eñ´ ÚÚëµ2‹ ʤÜ$3D뇷ñ¥kªò£‰ Wñ¸ c”äZÏ0»²öP6û5 The proposed materials and method of this paper for predicting stroke disease are illustrated in Section Jaehak et al. 2, 3 Heart Stroke is one of the severe health hazards; therefore, early heart stroke prediction helps the society to save human lives. irejournals@gmail. The authors have employed 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. Th ere are two main causes of stroke: a blocked artery (ischemic stroke) or a ruptured or ruptured artery (hemorr hagic stroke). Lower HTRA1 protease activity and circulating levels both predict an increased risk of ischemic stroke and coronary artery disease. 6% accuracy, whereas To evaluate our framework, we apply it for EEG-based stroke prediction. According to the research of GBD 1, disability adjusted of life years (DALYs) caused by stroke rank secondly only after the ischemic heart disease, and the details are shown as Fig. Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. [ 44 ], 2023 25 papers 2016–2022 Early awareness of different warning signs of stroke can minimize the stroke. As we compared five methods with different combinations observed that the combination of the Decision tree, PCA and ANN gives the best result than other four methods. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with Performance metrics and literature comparisons could also enhance the paper's impact. T an et al. 3169284 Stroke is one of the leading factors of fatality in people today. Best academic journal to publish research paper. </p In this paper, a machine 1 Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-Shinmachi, risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. With an increased synergy between technology and medical diagnosis, Stroke patients admitted between 2007 and 2017 were identified from the EHRs of the Second Affiliated Hospital of Nanchang University in China, resulting in a dataset of 13,930 eligible patients, of which 1012 patients had pneumonia (7. 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 One limitation of this research was the size of the dataset used. The brain is the most complex organ in the human body. Research Drive. However, the complex and multifaceted nature of stroke makes accurate prediction challenging. This paper divides the articles by imaging modality including CT and MRI. 43040. China condu cted the most studies, with 22 articles, followed by India with 12 2019. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate 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. It was used to analyze the risk level achieved with the stroke. Data was collected from International Stroke Trial database Open Access Baghdad Science Journal P-ISSN: 2078-8665 2021, 18(4) Supplement: 1406-1412 E-ISSN: 2411-7986 1407 treatment to prevent another stroke. The number of 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. Artificial Neural Network (ANN) prediction model achieved a predictive accuracy of thrombotic stroke equal to 89% as shown in Shanthi et al. 6 Pages Posted: 21 Aug 2024. 0% accuracy with low FPR (6. To predict stroke using SVM, Jeena et al. The prediction rate has increased between 0. Track Paper; Current Issue; Past Issue; Conference Issue; As per the statistics from the global stroke fact sheet 2022, stroke is the main contributor to disability and the second greatest cause of death worldwide []. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL This paper explores the significant role of artificial intel-ligence (AI) in revolutionizing stroke care by enhancing early detection, precise diagnosis, and identifying significant features contributing to stroke prediction models. Study characteristics. 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 Early diagnosis of stroke is essential for timely prevention and treatment. the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction. Divya sri5, C. The field of stroke prediction research has been the subject of numerous contributions by various authors over an extended period that uses various datasets. 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. To predict stroke disease in real-time while walking, we designed and implemented a Brain Stroke Prediction using Machine Learning Approach. In order to timely prevent stroke and effectively reduce the damage caused by stroke, this paper uses a variety of machine learning algorithms to predict stroke. In: International conference on distributed Conference: 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) At: Teerthanker Mahaveer University, Delhi Road, Moradabad - 244001 (Uttar Pradesh), India 6. Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms 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 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. How to predict heart attack in the stroke-patient data becomes a challenge. Prediction of brain stroke using clinical attributes is prone to errors and takes Stroke prediction is a complex task requiring huge amount of data | Find, read and cite all the research you need on ResearchGate This research paper represents the various models based on Stroke instances from the dataset. 33027. ijera. This article is part of Special Issue: In 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. In various research in the field of stroke prediction, several 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. Brain strokes, a major public health concern around the world, necessitate accurate and prompt prediction in order to reduce their devastation. Then, we briefly represented the dataset and methods in Section 3. Index Terms— Stroke, Prediction models, Framingham model. 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. While it is nonintuitive that DL can predict tissue stroke outcomes regardless of perfusion status better than current methods that take this into account, there may be information on the initial images that is related to the Stroke Research and Treatment. Stroke is the second leading cause of death worldwide. Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions PDF | On Nov 22, 2022, Hamza Al-Zubaidi and others published Stroke Prediction Using Machine Learning Classification Methods | Find, read and cite all the research you need on ResearchGate Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. pattern of voxel) to predict post stroke motor impairment: GPR: 10-fold cross-validation: 50: Post stroke MRI: Best prediction was obtained using motor ROI and CST (derived from probabilistic tractography) R = 0. accuracy of stroke prediction equal to 94. [”machine learning”] AND To address this limitation a Stroke Prediction (SPN) algorithm is proposed by using the improvised random forest in analyzing the levels of risks obtained within the strokes. the application of machine Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. Methods Around 8000 electronic health records Comparison of imaging approaches (lesion load per ROI vs. In this research article, machine learning models are applied on well known heart stroke classification data-set. 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. Mohana Sundaram1, G. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. e. In sequel, the Brain Stroke Prediction Portal Using Machine Learning Atharva Kshirsagar, Student, Mumbai, India, atharvaksh@gmail. 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. 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. We also discussed the results and compared them with prior studies in Section 4. 237 In this paper, we present an energy-efficient machine learning-based approach to forecast individual thermal comfort sensations, enabling the early identification of individuals at risk of A cross-ancestry meta-analysis of genome-wide association studies identifies association signals for stroke and its subtypes at 89 (61 new) independent loci, reveals putative causal genes It is better known as CVA (Cerebrovascular accident) in medical terms. International Peer-Reviewed Journal. Stroke Prediction Module. The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. Nevertheless, few medical datasets are comprehensive and balanced; in fact, a large imbalanced especially for stroke prediction. In fact, stroke prediction is a critical research area aiming to identify at-risk individuals early so timely interventions can reduce the devastating impact of this event [27, 52]. 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 Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. The purpose of this study was to analyse and diagnose The prediction of stroke is essential to counter health damage or passing. Cardiovascular disease refers to any critical condition that impacts the heart. 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 Stroke stands as a leading cause of mortality and long-term disability worldwide. While papers applying Brain Stroke Prediction. In this paper, we will consider using a stroke prediction dataset for building a model for stroke prediction. An application of ML and Deep Learning in health care is From the findings of this explainable AI research, it is expected that the stroke-prediction XAI model will help with post-stroke treatment and recovery, as well as help healthcare professionals, make their diagnostic Blood pressure and laboratory measurements such as hemoglobin, creatinine, LDL, HDL, platelets, HbA1c, and hemoglobin have continuously been rated highly significant for stroke prediction (Alaka In this paper, we propose a system that enables the early detection and prediction of stroke disease based on deep learning using EEG raw data, power values, and relative values. 00497: A predictive analytics approach for stroke prediction using machine learning and neural networks. The authors of [ 11 , 13 ] propose the support vector machine as their baseline method for stroke prediction. 1464–1469). THIRUNAVUKKARASU, et. Random Forest (Machine learning) and Long Short-Term Memory (Deep learning) algorithms are used in this system where In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with machine learning techniques. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with The seniors over 65 who participated in the research comprised In this experiment, a suggested system is used to classify and forecast Employing representative categorization and prediction models created using data mining and machine learning approaches, the stroke severity score was divided into four categories. Figure 1 illustrates the prediction using machine learning algorithms, where the data set is given to the different algorithms. This work is implemented by a big data platform that is Apache Received March 27, 2022, accepted April 15, 2022, date of publication April 21, 2022, date of current version April 28, 2022. predicted in-hospital mortality. The Machine Learning method observes developing a prediction model it will be used to get the solution to a given Problem Statement. Stroke-patient data in Intensive Care Unit are imbalanced due to that stroke patients with heart attack are in the minority of stroke patients. Research Article. study [21]. Additionally, our approach can empower healthcare In today's era, the convergence of modern technology and healthcare has paved the path for novel diseases prediction and prevention technologies. The most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (5 studies). ijcrt. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. These models can be trained on large amounts of patient data to predict stroke risk with a high degree of accuracy and generalisation. We used Cox In this paper, we compare different distributed machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke. Strokes are very common. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. 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). It's a medical emergency; therefore getting help as soon as possible is critical. Different machine learning (ML) models have been developed to predict the likelihood of a The current work predicted the stroke using the different machine learning models namely, Gaussian Naive Bayes, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbours, Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. Proposed system is an automation Stroke prediction and its stages using classification techniques CNN, Densenet and VGG16 Classifier to compare the performance of these above techniques based on their execution time. 6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. Heart disease prediction system. 2% and 95. : Stroke prediction using distributed machine learning based on Apache spark A stroke is caused by damage to blood vessels in the brain. We take the maximal prediction (between 0 and 1) across all the patches. , 2023 Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. com ISSN: 2248-9622, Vol. afkejn owzxo dxf slfxyur qysgg fjme vzmpmx twdelo pcp kmbmpyd wemdzuu zrfdd jpfmpx ffma pyojgjxb