Stroke prediction using machine learning. Ischemic Stroke, transient ischemic attack.

Stroke prediction using machine learning 5 million Chinese adults J. Stroke is the second leading cause of death worldwide. Mar 23, 2022 · 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. Early prediction of the stroke helps the patient to would have a major risk factors of a Brain Stroke. Feb 11, 2022 · Hung C-Y, Chen W-C, Lai P-T, Lin C-H, Lee C-C, editors. Keywords: machine learning, artificial intelligence, deep learning, stroke diagnosis, stroke prognosis, stroke outcome prediction, machine learning in medical imaging Nov 21, 2024 · This document summarizes a student project on stroke prediction using machine learning algorithms. Jan 23, 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. The prediction and results are then checked against each other. Am. , stroke occurrence), since, in many cases, until all clinical symptoms are manifested and experts can make a definitive diagnosis, the results are essentially irreversible. By applying machine learning algorithms to stroke, we developed a novel approach to diagnosis and treatment that surpasses manual judgment in sensitivity and significantly improves Apr 1, 2022 · Background: There have been multiple efforts toward individual prediction of recurrent strokes based on structured clinical and imaging data using machine learning algorithms. 36. Nov 2, 2023 · Shareefunnisa S, Malluvalasa SL, Rajesh TR, Bhargavi M (2022) Heart stroke prediction using machine learning. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart disease using Jul 24, 2024 · In [] the authors used machine learning to predict ischemic stroke. 2018. x = df. 9 (2023). wo In a comparison examination with six well-known May 9, 2021 · Matthew Chun, Robert Clarke, Benjamin J Cairns, David Clifton, Derrick Bennett, Yiping Chen, Yu Guo, Pei Pei, Jun Lv, Canqing Yu, Ling Yang, Liming Li, Zhengming Chen, Tingting Zhu, the China Kadoorie Biobank Collaborative Group, Stroke risk prediction using machine learning: a prospective cohort study of 0. The algorithms present in Machine Learning are constructive in making an accurate prediction and give correct analysis. IEEE; 2017. To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults. In studies of stroke risk prediction among the general population, some studies focused on lab variables like blood biomarkers, urine biomarkers and genetic variables 15 , 16 . in [17] compared deep learning models and machine learning models for stroke prediction from electronic medical claims database. Machine learning applications are becoming more widely used in the health care sector. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% Explore and run machine learning code with Kaggle Notebooks | Using data from National Health and Nutrition Examination Survey Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. PeerJ Comput. Dec 26, 2021 · 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 Schwartz L, Anteby R, Klang E et al (2023) Stroke mortality prediction using machine learning: systematic review. 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 Jun 30, 2022 · A predictive analytics approach for stroke prediction using machine learning and neural network soumyddbrata Dev a,b, Hewei Wang c,d, Chidozie Shamrock Nwosu, Nishtha Jain, Bharadwaj Veeravalli Dec 27, 2024 · Abstract page for arXiv paper 2501. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. Various machine learning algorithms, including Decision Trees, Support Vector Apr 16, 2023 · Heart Stroke Prediction using Machine Learning Vinay Kamutam *1 , Marneni Yashwant *2 , Prashanth Mulla *3 , Akhil Dharam *4 *1 Computer Science and Engineering, Sir Padampat Singhania University Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. INTRODUCTION 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. 2018;15: 1953–1959. Those who suffer from stroke, if luckily survived, may also suffer from expensive medical bills and even disability. Article PubMed PubMed Central Google Scholar Hassan A, Gulzar Ahmad S, Ullah Munir E, Ali Khan I, Ramzan N. Implementing a combination of statistical and machine-learning techniques, we explored how Early Stroke Prediction Using Machine Learning Abstract: Stroke is one of the most severe diseases globally, and it is directly or indirectly responsible for a considerable number of deaths. Machine learning algorithms are Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction. Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients. IEEE/ACM Trans Comput Biol Bioinform. A Mini project report submitted in. Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. Jan 1, 2024 · In this work, the machine learning (ML) and deep learning (DL) techniques in stroke risk prediction were evaluated, assessing their effectiveness and application in diverse contexts. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. In Mar 17, 2024 · Methods: To develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as . proposed a pre-detection and prediction method for machine learning and deep learning-based stroke diseases that measure the electrical activities of thighs and calves with EMG biological signal sensors, which can easily be used to acquire data during daily activities. It is a big worldwide threat with serious health and economic implications. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC). Therefore, we Dec 15, 2022 · Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. It can be Feb 23, 2024 · The research contributes to the growing literature on machine learning applications in healthcare by presenting a holistic approach to stroke prediction. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes Oct 15, 2021 · In this study of prehospital stroke prediction using machine learning, the algorithm using XGBoost had a high predictive value for strokes and stroke subcategories including LVO. 31-43, 2022 Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. This study proposes an accurate predictive model for identifying stroke risk factors. Jan 20, 2023 · The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. Ivanov et al. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. In this research work, with the aid of machine learning (ML Dec 28, 2024 · Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. 5 million Chinese adults, Journal of the American Medical Informatics Association Nov 1, 2022 · Stroke risk prediction using machine learning: A prospective cohort study of 0. ” Jul 30, 2021 · Objective: To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults. An ML model for predicting stroke using the machine learning technique is presented in [1]. 828–0. 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. Some of these efforts resulted in relatively accurate prediction models. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. In addition to conventional stroke prediction, Li et al. Heart diseases have become a major concern to deal with as studies show that the number of deaths due to heart diseases has increased significantly over the past few decades in India. Keywords - Machine learning, Brain Stroke. Strokes are very common. This paper is based on predicting the occurrence of a brain Dec 10, 2022 · Brain Stroke is considered as the second most common cause of death. 1719 - 1727 , 10. 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. 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. Hung et al. stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. J Neurol Sci 444:120529 [ DOI ] [ PubMed ] [ Google Scholar ] Sethi R, Hiremath JS, Ganesh V et al (2021) Correlation between stroke risk and systolic blood pressure in patients over 50 years with uncontrolled hypertension: results Dec 16, 2022 · 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. In this thorough analysis, the use of machine learning methods for stroke prediction is covered. 49% and can be used for early Oct 1, 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. Results indicate that while random forest achieves high accuracy, logistic regression provides a balanced sensitivity-specificity trade-off. Factors such as the data quality, the choice of features, and the choice of algorithm can impact how well models Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. ˛e proposed model achieves an accuracy of 95. We evaluated models for stroke risk at varying intervals of follow-up (<9 years, 0–3 In this study, we propose a machine learning-based approach for the prediction of stroke and heart disease risk. RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data. They preprocessed the data, addressed imbalance, and performed feature engineering. Depending on the area of the brain affected and amount of time, the blood supply blockage or bleeding can cause permanent damage or even lead to death. The paper reviews 12 studies on machine learning for stroke prediction, focusing on techniques, datasets, models, performance, and limitations. This review synthesizes findings from recent studies focusing on ML approaches for stroke prediction, emphasizing algorithmic performance, feature selection Jun 25, 2020 · 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 Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. 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. drop(['stroke'], axis=1) y = df['stroke'] 12. This paper describes a thorough investigation of stroke prediction using various machine learning methods. Feb 5, 2024 · The future scope of using machine learning for heart stroke risk prediction includes developing more accurate models, personalized risk assessment, integration with wearable technology, early detection of stroke, and population-level risk prediction. 1109/TCBB. An early intervention and prediction could prevent the occurrence of stroke. Med. The number of people at risk for stroke Dec 5, 2021 · Methods. 2811471 [Google Scholar] 13. Keywords: Stroke, Thrombolysis, Prediction, Machine learning, Imaging Jun 22, 2021 · For example, Yu et al. The intention of this newsletter is to use machine learning techniques to predict practical effects in patients three months after stroke. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. According to the performance test, weighted voting Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. This research investigates the application of robust machine learning (ML) algorithms, including According to the World Health Organization (WHO). Jan 15, 2024 · Risk factor prediction of stroke using machine learning and deep learning models: Stroke, a leading cause of disability and death globally, is influenced by a variety of risk factors, which are crucial to identify for its prevention and management. Methods We searched PubMed and Web of Science Nov 14, 2024 · An explainable machine learning pipeline for stroke prediction on imbalanced data. in. With a mortality rate of 5. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model Oct 27, 2022 · Request PDF | Stroke Prediction using Distributed Machine Learning Based on Apache Spark | Stroke is one of death causes and one the primary causes of severe long-term weakness in the world. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. However, acquiring clinical and imaging data is typically possible at provider sites only and is associated with additional costs. The results of several laboratory tests are correlated with stroke. Aug 20, 2024 · This study focuses on the intricate connection between general health, blood pressure, and the occurrence of brain strokes through machine learning algorithms. I. The paper is published in 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) in Noida, India. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods. M. Machine Learning. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Dec 2, 2024 · Various Machine Learning (ML) and Deep Learning (DL) models have been developed to predict stroke occurrence. Therefore, the aim of Jan 15, 2023 · Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. Methods— This Monteiro M, Fonseca AC, Freitas AT, Pinho E Melo T, Francisco AP, Ferro JM, et al. Early detection of heart conditions and clinical care can lower the death rate. We identify the most important factors for stroke prediction. train and test data. They experimentally verified an accuracy of more than Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. Methods— This Oct 1, 2024 · The use of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), has the potential to aid in stroke diagnosis and significantly advance healthcare. proposed a framework for the early prediction of stroke using various machine learning classifiers such as LR, SGD, DT, AdaBoost, Gradient Boosting Classifier (GBC), XGBoost (XGB), and multilayer perceptron (MLP) and compared them with the proposed weighted voting classifier. May 20, 2024 · Machine learning models have shown potential in stroke prediction. 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. Dec 1, 2022 · Brain Stroke Prediction by Using Machine Learning . Google Scholar Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. The students collected two datasets on stroke from Kaggle, one benchmark and one non-benchmark. Fetching user details through web app hosted using Heroku. Tan et al. , 28 ( 8 ) ( 2021 ) , pp. The work of Ahmed et al. We report our results on a balanced dataset created via sub-sampling techniques. In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. 5 million per year, it ranks as the second leading cause of death globally. Machine learning algorithms have been well suited and their flexibility in predicting stroke risk by analyzing large datasets of patient information. Machine Learning Based Approach Using XGboost for Heart Stroke Prediction. In Journal of Neutrosophic and Fuzzy Systems (JNFS) Vol. The prediction of stroke using machine learning algorithms has been studied extensively. The papers have published in period from 2019 to August 2023. Nov 1, 2022 · We use machine learning and neural networks in the proposed approach. Discussion This study demonstrated that the use of machine learning models can accurately predict long-term outcomes in acute stroke patients. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. In this study, we propose a machine learning-based approach for the prediction of stroke and heart disease risk. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. We searched PubMed, Google Scholar, Web of Science, and IEEE Xplore ® for relevant articles using various combination of the following key words: “machine learning,” “artificial intelligence,” “stroke,” “ischemic stroke,” “hemorrhagic stroke,” “diagnosis,” “prognosis,” “outcome,” “big data,” and “outcome prediction. Various machine learning algorithms, including Decision Trees, Support Vector Oct 3, 2024 · Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). The project provided speedier and more accurate predictions of stroke s everity as well as effective Apr 25, 2022 · examination of machine learning prediction algorithms in the literature. Oct 15, 2024 · Stroke prediction research has witnessed significant advancements through the application of machine learning (ML) techniques, contributing to improved accuracy and timely interventions. To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in Sep 15, 2022 · We set x and y variables to make predictions for stroke by taking x as stroke and y as data to be predicted for stroke against x. in International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06?07, 2021. This study presents a new machine learning method for detecting brain strokes using patient information. Aim is to create an application with a user-friendly interface which is easy to navigate and enter inputs. It is the world’s second prevalent disease and can be fatal if it is not treated on time. [PMC free article] 37. , 2023: 25 papers: 2016–2022: They review several papers aiming to answer three research questions: RQ1: What are the data needed for predicting ischemic stroke using deep learning? Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. The paper compares different machine learning models for stroke prediction and finds that AdaBoost, XGBoost and Random Forest Classifier have the highest accuracy. This review provides an outlook on recent research on stroke prediction using machine learning, including the types of data used, the algorithms employed, and the performance metrics reported. 2, No. The suggested system's experiment accuracy is assessed using recall and precision as the measures. 1093/jamia/ocab068 View in Scopus Google Scholar stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. JoonNyung Heo et al Machine Learning for Stroke Outcome Prediction 1265 0. The data-base contains information on 541 patients at Santa Maria sanatorium. 846 [95% CI, 0. Very less works have been performed on Brain stroke. Predictive modelling and identification of key risk factors for stroke using machine learning. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. J Pharmaceut Negative Results 2551–2558. g. in [18] used machine learning approaches for predicting ischaemic stroke and thromboembolism in atrial brillation. A variety of data mining techniques are employed in the health care industry to aid in diagnosing and early detection of illnesses. 10. published in the 2021 issue of Journal of Medical Systems. Jul 1, 2023 · Dhillon S, Bansal C, Sidhu B. Prediction of Stroke Using Machine Learning. Bachelor of Technology . Machine learning and data mining play an essential role in stroke forecasting, such as support vector machines, logistic regression, random forest classifiers and neural networks. Machine learning is a form of artificial Stroke is the fifth cause of death in the United States, according to the Heart Disease and Stroke Statistics 2020 report. The partial fulfilment of the requirements f or the a ward of the degree of. Ischemic Stroke, transient ischemic attack. 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. Stroke Detection and Prediction Using Deep Learning Techniques and Machine Learning Algorithms (National College of Ireland, 2022). Informatics Assoc. Rehman, A. We predict unknown data using machine learning algorithms. - msn2106/Stroke-Prediction-Using-Machine-Learning Chandramohan, R. Sci. Apr 28, 2024 · Feature extraction is a key step in stroke machine-learning applications, as machine-learning algorithms are widely used for feature classification and prediction. Jan 25, 2023 · The use of Artificial Intelligence (AI) methods (Big Data Analytics, ML, and Deep Learning) as predictive tools is particularly important for brain diseases (e. Early detection using deep learning (DL) and machine The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" Mehta, Adhikari, and Sharma are the authors. The brain is the most complex organ in the human body. Face to this This project, ‘Heart Stroke Prediction’ is a machine learning based software project to predict whether the person is at risk of getting a heart stroke or not. Jun 9, 2021 · A model using data science and machine learning was created by Rodrí guez [8] for stroke prediction. In this paper, we present an advanced stroke detection algorithm Prediction of stroke is a time consuming and tedious for doctors. Google Scholar; 20 ; Akash K, Shashank HN, Srikanth S, Thejas AM. Jan 1, 2019 · Many researchers have contributed to applying various sampling algorithms and machine learning models to predict stroke. 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 authors examine May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. The present study aimed to confirm whether sudden metabolic Aug 1, 2023 · Emon et al. This repository is a comprehensive project focusing on the prediction of strokes using machine learning techniques. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. Stroke Prediction Using Machine Learning (Classification use case) machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction Updated Jan 11, 2023 Jan 15, 2023 · The heterogeneity between studies, the high risk of bias and the lack of external validation emphasize that although much progress is witnessed using machine learning algorithms in predicting stroke their implementation in the real-world setting is limited and the use of ML for stroke mortality prediction is still in the research stage. Jun 26, 2024 · Unlike traditional prediction models that use selected variables for computation, machine learning techniques can easily incorporate a large number of variables as all computations are performed Mar 2, 2024 · Brain stroke is a Cerebrovascular accident that is considered as one of the threatening diseases. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. -To teach the computer machine learning algorithms use training data. Introduction: “The prime objective of Using the publicly accessible stroke prediction dataset, the study measured four commonly used machine learning methods for predicting brain stroke recurrence, which are as follows: Random forest Decision tree using data mining and machine learning approaches, the stroke severity score was divided into four categories. The dataset utilized comprises a comprehensive set of demographic, clinical, and lifestyle factors collected from a diverse population sample. However, no previous work has explored the prediction of stroke using lab tests. 541; Table III in the online-only Data Supplement). The works previously performed on stroke mostly include the ones on Heart stroke prediction. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. In our model, we used a machine learning algorithm to predict the stroke. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke Sep 8, 2023 · Stroke Prediction Using Machine Learning Abstract: A stroke is a serious medical emergency that happens when bleeding or blood clots cut off the blood flow to a part of the brain. 00048: Stroke Prediction using Clinical and Social Features in Machine Learning Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. 2 METHODS Feb 7, 2025 · The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. 2, PP. Materials and methods: We evaluated models for stroke risk at varying intervals of follow-up (<9 years, 0-3 years, 3-6 years, 6-9 years) in 503 842 adults without prior 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. , (2019) proposed distributed machine learning prediction of stroke disease is useful for prevention or early treatment intervention. 865] for the logistic regression model, P=0. 2022;12(10):2392. Comparative analysis and numerical results reveal that the Random Forest algorithm is best suited for stroke prediction. et al. Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. Age, heart disease, average glucose level are important factors for predicting stroke. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. Diagnostics. The individual characteristics of patients including clinical data and demographic data were Oct 1, 2024 · 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. This research highlights the effectiveness of Federated Learning (FL), a decentralized training approach that bolsters privacy while preserving model performance. Thus, future prospective, multicenter studies with standardized reports are cruci … A bibliometric analysis showed that most studies have focused on using machine learning to improve stroke risk prediction, diagnosis, and outcome prediction 14. 6 days ago · Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. qdplzwh ahmw wvovb vevuzec jddkz smo kyzeuo hxg jjz vnmd pgaxaf pktltm jztnmaq wjdfn bjnrvwk