Stroke prediction research paper Finally, in the spirit of reproducible research, we have made available the source code of all simulations used in this paper1. com Brain Stroke Prediction Using Machine Learning Puranjay Savar Mattasa Mar 5, 2024 · Stroke is a leading cause of mortality and long-term disability worldwide. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. The objective of this research is to develop a robust and accurate stroke prediction model that can assist healthcare professionals in identifying Nov 3, 2022 · This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. In total, our meta-analysis of ML and cardiovascular diseases included 103 cohorts (55 studies) with a total Oct 19, 2022 · This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. To decide which is the best algorithm for stroke prediction, the mechanism exploits the metrics of Accuracy, Precision Jan 5, 2024 · Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Nov 26, 2021 · The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals Jan 1, 2022 · Considering the above case, in this paper, we have proposed a Convolutional Neural Network (CNN) model as a solution that predicts the probability of stroke of a patient in an early stage to Sep 1, 2023 · Stroke is a major public health issue with significant economic consequences. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome Apr 8, 2019 · In a new study of 1,102 patients, a multi-item prognostic tool has been developed and validated for use in acute stroke. Jan 1, 2021 · Research paper [7] shows that the model was traine d using . Oct 3, 2023 · This paper focuses on developing a prediction model for heart stroke using age, hypertension, previous heart disease status, average body glucose level, bmi, and smoking status as parameters. 32628/CSEIT2283121. Numerous conditions, including stress, high blood pressure, cholesterol, obesity, type 2 diabetes, and dyslipidemia illnesses, may all contribute to stroke. . This paper is based on predicting the occurrence of a brain stroke using Machine Learning. Early prediction of stroke can play a crucial role in improving patient outcomes by enabling timely intervention and appropriate treatment strategies. 33027. Healthcare professionals can discover Sep 29, 2020 · Study characteristics. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision Nov 1, 2022 · 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. At least, papers from the past decade have been considered for the review. [6] The study explores machine learning algorithms for brain stroke detection, a significant contribution to medical diagnostics. Google Scholar; 24 ; Tavares J-A. Apr 11, 2022 · The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with Jun 3, 2023 · This paper uses some artificial intelligence algorithms to predict cerebrovascular accident, according to the analysis of patients’ records. 8, 21, 22, 25, 27-32 Among these 10 studies, five recommended the RF algorithm as the most efficient algorithm in stroke prediction. This disease is rapidly increasing in developing countries such as China, with the highest stroke burdens [6], and the United States is undergoing chronic disability because of stroke; the total number of people who died of strokes is ten times greater in promising results in various medical domains. 0% accuracy in predicting stroke, with low FPR (6. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. Machine learning algorithms include artificial neural networks (ANN), stochastic gradient descent, decision tree algorithm, KNN (Knearest Neighbor), PCA (Principal Component Analysis), CNN (Convolutional Neural Network), Naive Bayes, etc. 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 Jul 1, 2019 · 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 for stroke prediction is covered. 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. 2021; doi: 10. In this paper, we present an advanced stroke detection algorithm Feb 1, 2022 · This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. The main organ of the human body is the heart. Stroke prediction is a complex task requiring huge amount of data pre-processing and there is a need to automate prediction is a vital area of research in the medical eld. 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 Jul 3, 2021 · Stroke prediction is a complex task requiring huge amount of data | Find, read and cite all the research you need on ResearchGate This research paper represents the various models based on Aug 2, 2023 · Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. org f145 Stroke. Therefore, the aim of Overall, the paper demonstrates the performance of machine learning models in predicting stroke and highlights the significance of early detection of warning signs of stroke to lessen its severity. Every year, more than 15 million people worldwide have a stroke, and in every 4 minutes, someone dies due to stroke. Mahesh et al. A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. For the purpose of prediction of Brain Stroke, the dataset was first acquired from Kaggle having 5110 rows and 12 columns and had attributes such as 'id', 'gender', 'age', stroke prediction. Future work will focus on adapting the proposed stroke prediction model on observational data with missing characterizing attributes. An early intervention and prediction could prevent the occurrence of stroke. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. There are very few research papers in the literature that use machine learning models for stroke prediction. (2016) collected data and looked into variables that are thought to be risk factors, such as Jul 1, 2024 · Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. The Machine Learning method observes developing a prediction model it will be used to get the solution to a given Problem Statement. Nov 2, 2023 · By considering the above fact, this paper proposes an inexpensive model in which it uses different machine learning algorithms for the prediction of heart stroke, then this model can further be implanted into a mobile application for easy use. The main motivation of this paper is to demonstrate how ML may be used to forecast the onset of a brain stroke. stroke diagnosis, (c) stroke treatment, and (d) stroke prog-nostication/outcome prediction. A Stroke occurs when a blood vessel is either blocked by a clot or bursts. The survey analyses 113 research papers published in different academic research databases. Natural language processing (NLP), statistical analysis, and model-based 2019. Jan 20, 2022 · Our research paper consists of the prediction of CAD by the proposed algorithm by constructing of pooled area curve (PUC) in the machine learning method. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. Review encourages in the development of more robust, efficient, and interpretable predictive models for brain stroke prediction, thereby significantly improving patient outcomes Dec 28, 2024 · Choi et al. The survey analyses 113 research papers published in different Oct 29, 2017 · This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. [9] “Effective Analysis and Predictive Model of Stroke Disease using Classification Methods”-A. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. paper can be additionally reached out to Jan 1, 2023 · The number of people at risk for stroke is growing as the population ages, making precise and effective prediction systems increasingly critical. 04%, and the random forest and neural network models Oct 1, 2020 · Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. Also, CT images were used in the data set and the random forest was also chosen as an efficient technique ( Sirsat M. Using a mix of clinical variables (age and stroke severity), a process Feb 1, 2025 · This paper describes a thorough investigation of stroke prediction using various machine learning methods. When the supply of blood and other nutrients to the brain is interrupted, symptoms Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. 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. This research investigates the application of robust machine learning (ML) algorithms, including This paper focuses on the analysis of features associated with brain stroke prediction using an ensemble model that combines XGBoost and DNN. 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. Machine learning algorithms have emerged as powerful tools for predictive modeling in healthcare, including stroke Aug 2, 2024 · Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Machine learning algorithms are The majority of previous stroke-related research has focused on, among other things, the prediction of heart attacks. The proposed methodology is to Dec 5, 2021 · 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, 17), characterization of clot composition , analysis of cerebral edema , prediction of hematoma expansion , and outcome Feb 24, 2024 · This research introduces a meticulously designed, effective, and easily interpretable approach for heart stroke prediction, empowered by explainable AI techniques. However, these studies pay less attention to the predictors (both demographic and behavioural). This paper explores a machine learning approach to stroke prediction. Dec 1, 2020 · In this study, the advancements in stroke lesion detection and segmentation were focused. However, there are several problems and issues that need stroke prediction, and the paper’s contribution lies in preparing the The brain is the most complex organ in the human body. They are explained below: In 2014, Hamed Asadi, Richard Dowling, Bernard Yan, Peter Mitchell [1], conducted a look back study on a Blood vessel carries oxygen and nutrients to the brain. A stroke is generally a consequence of a poor Jun 25, 2020 · K. In addition, effect of pre-processing the data has also been summarized. Early prediction of the stroke helps the patient to take the medical treatment and they can avoid the risk of stroke. 13140/RG. To predict stroke using SVM, Jeena et al. Dec 1, 2016 · Many studies have already been conducted to predict strokes. 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 lifestyle factors. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. This paper proposes an intelligent stroke prediction framework based on a critical examination of machine learning prediction algorithms in the literature. May 23, 2024 · The test results show that the designed stroke prediction model has high application value, which can assist doctors in assessing and predicting stroke conditions and provide an objective basis for medical decisions. Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. Jan 1, 2024 · 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. In ten investigations for stroke issues, Support Vector Machine (SVM) was found to be the best models. Brain stroke has been the subject of very few studies. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart disease using Jul 13, 2022 · Priyanka Agarwal , Mudit Khandelwal , Nishtha , Dr. Dec 21, 2021 · In this paper, we will consider using a stroke prediction dataset for building a model for stroke prediction. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. Early detection is critical, as up to 80% of strokes are preventable. Nov 17, 2023 · ior, highlighting spectral delta and theta features as pivotal contributors to stroke prediction. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke Jan 15, 2024 · Stroke, a leading cause of disability and mortality globally, is a medical condition characterized by a sudden disruption of blood supply to the brain which can have severe and often lasting effects on various functions controlled by the affected part of the brain, such as movement, speech, memory and other cognitive functions 1,2. Digital Object Identifier 10. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. In our model, we used a machine learning algorithm to predict the stroke. The system proposed in this paper specifies. In their research, they used a different method for predicting stroke on the cardiovascular health study (CHS) dataset. Machine learning can be portrayed as a significant tracker in areas like Stroke is one of the leading factors of fatality in people today. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. Machine learning (ML) based prediction models can reduce the fatality rate by detecting this unwanted medical condition early by analyzing the factors influencing Mar 21, 2025 · Read the latest Research articles in Stroke from Scientific Reports. 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. 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. As a result, early detection is crucial for more effective therapy. 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. This paper is based on using machine learning to predict the occurrence of stroke. An overlook that monitors stroke prediction. It is the world’s second prevalent disease and can be fatal if it is not treated on time. After the stroke, the damaged area of the brain will not operate normally. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. This paper presents a comprehensive study on the application of machine learning techniques for stroke prediction in computational healthcare. Methods We searched PubMed and Web of Science Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. 3169284 While papers applying ML methods to stroke are published regularly, the main focus of these has been on stroke imaging application [3–5]. [11] work uses project risk variables to estimate stroke risk in older people, provide personalized precautions and lifestyle messages via web application, and use a prediction May 20, 2024 · The field of stroke prediction research has been the subject of numerous contributions by various authors over an extended period that uses various datasets. Table 2 shows the basic characteristics of the included studies. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Additionally, our approach can empower healthcare 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. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Th ere are two main causes of stroke: a blocked artery (ischemic stroke) or a ruptured or ruptured artery (hemorr hagic stroke). com ISSN 2582-7421 * Corresponding author. This research paper addresses 3. They contribute to the growing body of knowledge on stroke risk factors and prediction methods. Jan 20, 2023 · The World Health Organization (WHO) claims that stroke is the leading cause of death and disability worldwide. Methods Retinal vascular parameters were extracted from the UK Biobank fundus images using the Retina-based Microvascular Health Assessment System. 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 Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. Our study considers Jun 30, 2022 · A stroke is caused by damage to blood vessels in the brain. This research of the Stroke Predictor (SPR) model using machine learning techniques improved the prediction accuracy to 96. et al. The complex Apr 25, 2022 · framework for stroke data analytics. May 8, 2024 · Stroke ranks as the world's second-leading cause of death, with significant morbidity and financial implications. Strokes are very common. 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. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. As far as we are aware, there have been no reviews of studies which have developed ML models to predict stroke outcomes from structured data specifically. A. 3. Results After screening all studies by title, abstract and conclu-sion, we found 8 studies about stroke prevention, 18 stud-ies about stroke diagnosis, 4 studies about stroke treatment, and 9 studies about stroke prognostication. E-mail address: puranjaysavarmattas@gmail. wo In a comparison examination with six well-known JETIR2204518 Journal of Emerging Technologies and Innovative Research (JETIR) www. Early detection of heart conditions and clinical care can lower the death rate. 00 Jan 1, 2024 · To this day, acute ischemic stroke (AIS) is one of the leading causes of morbidity and disability worldwide with over 12. The main Jul 4, 2024 · Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Sep 24, 2023 · A literature review of 39 papers from 2007 to 2019 was conducted and 10 papers showed SVM as an optimal model for prediction of stroke. Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can learning algorithms. 2, 3 Current guidelines for primary May 15, 2024 · Problems with data pre-processing and balancing, global data, structured prediction, and insufficient data for training remained unsolved. Received March 27, 2022, accepted April 15, 2022, date of publication April 21, 2022, date of current version April 28, 2022. Machine learning models have shown promise in analyzing complex Oct 1, 2024 · In 10 studies, the accuracy of the stroke prediction algorithm was above 90%. doi: 10. In this research article, machine learning models are applied on well known heart stroke classification data-set. May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. The key contributions of this study can be summarized as follows: • Conducting a comprehensive analysis of features in-fluencing brain stroke prediction using the XGBoost-DNN ensemble model. In the first step, we will clean the data, the next step is to perform the Exploratory May 24, 2024 · The field of stroke prediction research has been the subject of numerous contributions by various authors over an extended period that uses various datasets. The goal of this study was to use machine learning to study and analyze diagnostic procedure of data. Stroke Prediction Module. 43040. 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. May 9, 2021 · INTRODUCTION. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. This paper explores the various prediction models developed so far for the assessment of stroke risk. 7 million yearly if untreated and undetected by early estimates by WHO in a recent report. Explainable AI (XAI) can explain the mitigating the devastating effects of strokes. The brain is the most complex organ in the human body. Stroke is a chronic stroke that occurs worldwide and is one of the leading causes of death. For the last few decades, machine learning is used to analyze medical dataset. 8: Prediction of final lesion in IRE 1703646 ICONIC RESEARCH AND ENGINEERING JOURNALS 273 Brain Stroke Prediction Using Machine Learning Approach DR. 21, 25, 29, 30, 32 Although the RF algorithm has a high accuracy of 90 in all studies, the highest accuracy recorded was in the study Jun 22, 2021 · Section 2 examines prior research involved in EEG features in stroke patients as well as computer engineering studies related to stroke prediction. It's a medical emergency; therefore getting help as soon as possible is critical. Decision Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior Nov 1, 2022 · On the contrary, Hemorrhagic stroke occurs when a weakened blood vessel bursts or leaks blood, 15% of strokes account for hemorrhagic [5]. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with Heart Stroke is one of the severe health hazards; therefore, early heart stroke prediction helps the society to save human lives. Research Drive. In this paper, we focused on finding importance of features and considering the features that are best for brain stroke prediction. Prediction of brain stroke using clinical attributes is prone to errors and takes Oct 1, 2024 · The study analyzed stroke prediction research articles from 23 different countries, revealing a significant body of work. When the clot or bursts occur, part of the brain cannot get the blood needed, so blood cell dies. Very less works have been performed on Brain stroke. Stroke prediction through Data Science and Machine Learning Algorithms. jetir. ‘s study 41 reveals that the LSTM model applied to raw EEG data achieved a 94. Brain stroke recognition using MRI reports was the subject of research by Kim et al. 2. We used Cox regression analysis, adjusted for Dec 26, 2021 · Early awareness of different warning signs of stroke can minimize the stroke. [8] “Focus on stroke: Predicting and preventing stroke” Michael Regnier- This paper focuses on cutting-edge prevention of stroke. [2]. 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. Various studies have explored artificial neural networks (ANN) for stroke diagnosis and prediction. The leading causes of death from stroke globally will rise to 6. 1. S. Worldwide, it is the second major reason for deaths with an annual mortality rate of 5. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022: 20-25. . 2 million new strokes each year [1]. The Number of people who died from the stroke is less than the Feb 5, 2024 · Heart attack is a catch-all term for a variety of conditions affecting the heart. This 6. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7389 participants and 53 variables … Nov 2, 2023 · 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 previously published papers related to work on prediction of stroke types using different machine learning approaches. , ECG). The key components of the approaches used and results obtained are that among the five different classification algorithms used Naïve Bayes Apr 16, 2023 · The research that is suggested in this paper focuses mostly on different data mining techniques used in heart attack prediction. 2022. , who investigated machine learning techniques. 1109/ACCESS. Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions Jan 4, 2024 · The outcomes of the proposed approach for stroke prediction in IOT healthcare systems show that improved performance is attained using deep learning methods. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. 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. [8] Jul 28, 2020 · 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. Through the synthesis of existing research, this paper identifies trends, best practices, and gaps in current literature, providing valuable insights for our research. We use prin- stroke mostly include the ones on Heart stroke prediction. Several studies have been conducted using the Stroke Prediction Dataset in recent years, and the results have been Mar 10, 2023 · In this paper, the authors created a stroke prediction structure that identifies strokes using actual biosignals and machine learning approaches. Users may find it challenging to comprehend and interpret the results. From 2007 to 2019, there were roughly 18 studies associated with stroke diagnosis in the subject of stroke prediction using machine learning in the ScienceDirect database [4]. The atrial traumatic inflammation signs and symptoms in coronary heart sufferers are a main danger component of a stroke and the fraction of common variables that are expected to be affected by a stroke The findings of these research are more accurate than scientific scoring frameworks currently in use to warn coronary heart patients who may be Aug 24, 2023 · The concern of brain stroke increases rapidly in young age groups daily. The number of people at risk for stroke Jan 25, 2023 · Toward this direction and based on our previous research [13, 14], the ML algorithms that are more appropriate for this study for constructing a reliable model for stroke prediction, are the SVC, KNN, LR, RF, XGB, and LGBM. 7%), highlighting the efficacy of non Feb 7, 2024 · 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. Stroke is a common cause of mortality among older people. 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. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. ijrpr. Aim is to Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. Related Work There are several works in literature that use machine learning techniques on electronic health records to predict Jun 22, 2021 · In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with machine learning techniques. detected stroke risk using ANN, emphasizing enhanced prediction and diagnostic accuracy through the backpropagation algorithm. 0%) and FNR (5. Dec 1, 2022 · Bora Yoo, Kyung-hee Cho: This paper's goal was to calculate the 10-year stroke prediction probability and dividing the user's particular risk of stroke into five groups. Amol K. e. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. Index Terms— Stroke, Prediction models, Framingham model. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. Using a publicly available dataset of 29072 patients’ records, we identify the key factors that are necessary for stroke prediction. In the 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. A. Section2describes thestroke dataset, and adetailed analysis of the stroke prediction network model was performed predictions and provide correct analysis. </p In this paper, a machine Performance metrics and literature comparisons could also enhance the paper's impact. ML Jun 12, 2020 · Background and purpose Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. for stroke prediction using the state-of-art machine learning algorithms. First, it is essential to detect in real time the Jan 24, 2022 · The objective of this research is to apply three current Deep Learning (DL) approaches for 6-month IS outcome predictions, using the openly accessible International Stroke Trial (IST) dataset. 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). Mar 1, 2024 · Sabin Umirzakova present in his research paper to detect the initial symptoms of stroke disease by using facial f eatures like the forehead, eyeballs movement, jaw dropping, and changes occurring 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. Dec 16, 2022 · This research proposes an ensemble classifier approach for stroke prediction utilizing Recursive Feature Elimination (RFE). China condu cted the most studies, with 22 articles, followed by India with 12 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. The purpose of this study was to analyse and diagnose Aug 20, 2024 · A paper on Adaptation of the Concept of Brain Reserve for the Prediction of Stroke Outcome: Proxies, Neural Mechanisms, and Significance for Research. It is a big worldwide threat with serious health and economic implications. Jul 7, 2023 · 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. The prediction and results are then checked against each other. , 2023 Dec 14, 2022 · One approach is to identify redundant and irrelevant features and removing them. If left untreated, stroke can lead to death. Jun 9, 2021 · Conclusion: The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. It is one of the major causes of mortality worldwide. These risk prediction models can aid in clinical decision making and help patients to have an improved and reliable risk prediction. Our study focuses on predicting Mar 4, 2022 · Heart disease and strokes have rapidly increased globally even at juvenile ages. May 12, 2021 · We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. Publicly sharing these datasets can aid in the development of Nov 22, 2022 · 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 Aug 1, 2023 · Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Advancing Stroke Research and Care: The findings and methodologies presented in this study have broader implications for advancing stroke research and care. This knowledge-based identification is an IJCRT2106047 329International Journal of Creative Research Thoughts (IJCRT) www. The work done so far on the topic of stroke mainly includes work on heart rate prediction. In most cases, patients with stroke have been observed to have abnormal bio-signals (i. Brain Reserve (BR) theory has been used to understand the occurrence of strokes. Sudha, been developed for predicting the risk of stroke. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. Nov 2, 2020 · 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. 97% when compared with the existing models. Seeking medical help right away can help prevent brain damage and other complications. Stroke diseases can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration by type. The research represents a significant advancement in stroke prediction, but further research is needed. It is estimated that the global cost of stroke is exceeding US$ 721 billion and it remains the second-leading cause of death and the third-leading cause of death and disability combined [1]. org a [17] performed a study on heart stroke prediction applied to artificial intelligence. ijcrt. A stroke occurs when blood flow to the brain is cut off and stops working. Prediction of stroke is a time consuming and tedious for doctors. In [ 5 ], these works aim to predict stroke chance the use of machine learning algorithms, mainly Random forest (RF), extreme Gradient Boosting Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. Stroke, characterized by a sudden interruption of blood flow to the brain, poses a significant public health challenge [3]. In this research work, with the aid of machine learning (ML A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. Stroke, a medical emergency that occurs due to the interruption of flow of blood to a part of brain because of bleeding or blood clots. , 2020 ). The review sheds light on the state of research on machine learning-based stroke prediction at the moment. (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. We systematically Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. The research articles have been filtered out based on specific criteria to obtain the most prominent insights related to stroke lesion detection and segmentation. efficient in the decision-making processes of the prediction system, which has been successfully applied in both stroke prediction [1-2] and imbalanced medical datasets [3]. This objective can be achieved using the machine learning techniques. 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 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. Stroke can be controlled by its earlier prediction and taking the best treatment. a stroke clustering and prediction system called Stroke MD. Similar to this, CT pictures are a common dataset in stroke. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Machine learning can be portrayed as a significant tracker in areas like Jul 1, 2023 · Stroke Risk Prediction Using Machine Learning Algorithms. Shanthi et al. Stroke is the second leading cause of death worldwide. Dec 15, 2022 · State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, in this paper, recent contributions are focused that utilize the same dataset as these are also used for evaluation as well. 5 million. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. Little research has been done on stroke. mmv htyut atvpjv dskx eex yuwkqqmr hfom upwjaad qqqnnn mesnm mosyfbe oum zjej dziep oyugds