Stroke prediction using machine learning. 2% and precision of 96.

Stroke prediction using machine learning There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke The existing research is limited in predicting whether a stroke will occur or not. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. The project aims to develop a model that can accurately predict strokes based on demographic and health data, enabling preventive interventions to reduce the Feb 1, 2025 · Objective This study aimed to develop and validate a machine learning-based predictive model for gait recovery in patients with acute anterior circulation ischemic stroke. Diagnostics. The results of several laboratory tests are correlated with stroke. in. 1111/ene. We use a set of electronic health records (EHRs) of the patients (43,400 patients) to train our stacked machine learning model 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. Front Neurol. 2% and precision of 96. 865] for the logistic regression model, P=0. Li Q, Chi L, Zhao W, et al. 190 , 105381 (2020). Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. Google Scholar; 20 ; Akash K, Shashank HN, Srikanth S, Thejas AM. Am. However, most stroke mortality can be prevented by identifying the nature of the Oct 1, 2020 · To be able to do that, Machine Learning (ML) is an ultimate technology which can help health professionals make clinical decisions and predictions. It is the world’s second prevalent disease and can be fatal if it is not treated on time. As a leading cause of death, strokes have been regarded as a dangerously impactful condition with little to no predictability. There may be drawbacks According to the World Health Organization (WHO). Machine learning algorithms have been well suited and their flexibility in predicting stroke risk by analyzing large datasets of patient information. 1007/978-3-030-3370 9-4_19. 04%, and the random forest and neural network models In this work, we aimed to predict the incidence of strokes using machine learning approaches. Int J Innov Res Engineer Manag. A machine learning model is approached for predicting the existence of stroke of a patient where the Random forest classifier outperforms the state-of-the-art models, including Logistic Regression, Decision Tree Classifier (DTC), K-NN. Thus, future prospective, multicenter studies with standardized reports are cruci … 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. Therefore, the aim of Nov 1, 2022 · Stroke risk prediction using machine learning: A prospective cohort study of 0. Methods— 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. [Google Scholar] 17. Machine learning applications are becoming more widely used in the health care sector. Methods Between May and November 2023, 237 patients with acute anterior circulation ischemic stroke were enrolled. 49% and can be used for early 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. tackled issues of imbalanced datasets and algorithmic bias using deep learning techniques, achieving notable results with a 98% Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. May 15, 2024 · Problems with data pre-processing and balancing, global data, structured prediction, and insufficient data for training remained unsolved. The suggested system's experiment accuracy is assessed using recall and precision as the measures. Nov 2, 2023 · Shareefunnisa S, Malluvalasa SL, Rajesh TR, Bhargavi M (2022) Heart stroke prediction using machine learning. Discussion This study demonstrated that the use of machine learning models can accurately predict long-term outcomes in acute stroke patients. Age, heart disease, average glucose level are important factors for predicting stroke. , 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? Jan 1, 2019 · Many researchers have contributed to applying various sampling algorithms and machine learning models to predict stroke. The project provided speedier and more accurate predictions of stroke s everity as well as effective 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 Hung et al. 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. These Machine (SVM), and ending with ensemble methods as hard and soft voting classifiers. P. Using a variety of machine learning methods, such as Decision Trees, k-nearest Neighbors (kNN), Naive Bayes, Support Vector Machine (SVM), Logistic Regres- sion, and Random Forest, we provide an ensemble approach for stroke prediction in this study Machine Learning for Brain Stroke: A Review Manisha Sanjay Sirsat,* Eduardo Ferme,*,† and Joana C^amara, *,†,‡ 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 International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06?07, 2021. Predicting strokes is essential to preventive health- care since it allows for early intervention and lowers the related morbidity and mortality. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta‐analysis. 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. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. 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 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. 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. It can be 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 using data mining and machine learning approaches, the stroke severity score was divided into four categories. Patients were randomly divided into training and validation sets at a 7:3 ratio. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. stroke prediction, and the paper’s contribution lies in preparing the dataset using machine learning algorithms. Machine learning is a form of artificial 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. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. ” 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. Machine learning algorithms are 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. Methods Programs Biomed. 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. 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. J Pharmaceut Negative Results 2551–2558. We developed a quantitative method to predict strokes before happening. 1161/STROKEAHA. Section 2 p resents the related works. The authors used Decision Tree (DT) with C4. Google Scholar Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. Ivanov et al. ac. The results obtained demonstrated that the DenseNet-121 classifier performs the best of all the selected algorithms, with an accuracy of 96%, Recall of 95. [Google Scholar] 23. 2021. patients/diseases/drugs based on common characteristics [3]. 2, 100032 (2022). Jul 2, 2024 · Stroke poses a significant health threat, affecting millions annually. Anal. , Raman B. Mar 2, 2024 · Brain stroke is a Cerebrovascular accident that is considered as one of the threatening diseases. However, acquiring clinical and imaging data is typically possible at provider sites only and is associated with additional costs. wo In a comparison examination with six well-known Jan 15, 2023 · Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Dec 15, 2022 · Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. The students collected two datasets on stroke from Kaggle, one benchmark and one non-benchmark. 2020;27:1656–1663. 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]. In Journal of Neutrosophic and Fuzzy Systems (JNFS) Vol. 2023;14:1039794. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. 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. AMOL K. 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 Nov 9, 2024 · Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. Brain stroke recognition using MRI reports was the subject of research by Kim et al. Apr 25, 2022 · examination of machine learning prediction algorithms in the literature. of Computer Science & Engineering CMRIT, Bangalore Karnataka, India akma16cs@cmrit. Our task is to examine existing patient records in the training set and use that knowledge to predict whether a patient in the evaluation set is Aug 21, 2024 · This article is part of the Research Topic Innovative Applications of Machine Learning and Cutting-Edge Tools for Stroke Prediction and Treatment Strategies View all 5 articles Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular This repository contains the code and documentation for a data mining project focused on stroke prediction using machine learning techniques. We identify the most important factors for stroke prediction. The paper is published in 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) in Noida, India. Informatics Assoc. 216 – 225, doi: 10. 541; Table III in the online-only Data Supplement). Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. Ischemic Stroke, transient ischemic attack. With a mortality rate of 5. Comparative analysis and numerical results reveal that the Random Forest algorithm is best suited for stroke prediction. 34 Whereas CHADS 2 and CHA 2 DS 2-VASc use 6–7 features to stratify stroke risk, an attention-based DNN model identified up to 48 features that influenced stroke risk using Mar 10, 2023 · In order to predict the heart stroke, an effective heart stroke prediction system (EHSPS) is developed using machine learning algorithms. In the existing method a stroke is predicted utilising various machine learning algorithms such as random forest, AdaBoost, stochastic gradient, decision tree, logistic regression, and naïve bays classifier etc. Introduction: “The prime objective of 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. 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. in [18] used machine learning approaches for predicting ischaemic stroke and thromboembolism in atrial brillation. Methods We searched PubMed and Web of Science 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. Gautam A. Our work also determines the importance of the characteristics available and determined by the dataset. Oct 13, 2022 · This study proposes a machine learning approach to diagnose stroke with imbalanced data more accurately. prediction of stroke disease is useful for prevention or early treatment intervention. Early detection of heart conditions and clinical care can lower the death rate. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thirty-one medical Oct 1, 2023 · Additionally, Tessy Badriyah used machine learning algorithms for classifying the patients' images into two sub-categories of stroke disease, known as ischemic stroke and stroke hemorrhage. Keywords - Machine learning, Brain Stroke. 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. 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. A stroke can be treated with the right medicine and recovered if it is detected early enough. in SHASHANK H N Interpretable Stroke Risk Prediction Using Machine Learning Algorithms 649. May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. Apr 22, 2023 · To predict a patient’s risk of having stroke, this project used machine learning (ML) approach on a stroke dataset obtained from Kaggle, the ANOVA (Analysis of Variance) feature selection method with and without the following four Classification procedures; Logistic Regression, K-Nearest Neighbor, Naïve Bayes, and Decision Tree, after which [7] [8] Anish Xavier, “Heart Disease Prediction using Machine Learning and Data Mining Technique”, International Journal of Engineering Research & Technology (IJERT); ISSN: 2278-0181; Published by, www. However, no previous work has explored the prediction of stroke using lab tests. In addition, several attempts in the machine learning field to build a stroke predictor module using different The brain stroke prediction module using machine learning aims to predict the likelihood of a stroke based on input data. The papers have published in period from 2019 to August 2023. Aug 1, 2023 · Emon et al. 1719 - 1727 , 10. Stroke. , who investigated machine learning techniques. JoonNyung Heo et al Machine Learning for Stroke Outcome Prediction 1265 0. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. 846 [95% CI, 0. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. 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. This comparative study offers a detailed evaluation of algorithmic methodologies and outcomes from three recent prominent studies on stroke prediction. 5 algorithm, Principal Component Jun 1, 2024 · we exp lore th e use of IoT devices and machine learning algorithms in the context of heat stroke prediction. et al. 14295. 9579648 The Cardiac Stroke Prediction System is a web-based application designed to help predict the likelihood of a stroke in patients based on entered symptoms. Apr 15, 2024 · Machine learning models that employ large datasets, including potential predictors, can improve prediction accuracy, as presented in the current study, for the prediction ischemic stroke in AF patients using ML models in comparison to CHA 2 DS 2-VASc, and provide graphical interpretation of the results using SHAP analysis. , (2019) proposed distributed machine learning An ML model for predicting stroke using the machine learning technique is presented in [1]. , 28 ( 8 ) ( 2021 ) , pp. in [17] compared deep learning models and machine learning models for stroke prediction from electronic medical claims database. 117. 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. This stroke prediction web browser for stoke prediction and its types using machine learning algorithms like KNN. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart disease using 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. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. Some of these efforts resulted in relatively accurate prediction models. Eur. 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. In this study, the classification of stroke diseases is accomplished through the application of eight different machine learning algorithms. Towards effective classification of brain hemorrhagic and ischemic stroke using CNN. The number of people at risk for stroke 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. Prediction of Stroke Using Machine Learning. Random Over Sampling (ROS) technique has been used in this work to balance the data. The work of Ahmed et al. Healthc. 2, No. Article PubMed PubMed Central Google Scholar Hassan A, Gulzar Ahmad S, Ullah Munir E, Ali Khan I, Ramzan N. 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. 1109/ICCCNT51525. The dataset includes demographic and health-related variables such as age, gender, heart disease, hypertension, and smoking status. The brain cells are not getting enough blood and oxygen. 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]. . 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. They preprocessed the data, addressed imbalance, and performed feature engineering. Other methods found in the literature are classification , neighbourhood-level impact based approach , Embolic Stroke Prediction , Prediction of NIH stroke scale and detection of ischemic stroke from radiology reports [26, 27] Hybrid machine learning approach scenario on genetic algorithms to improve characteristic features. Hung et al. 2018;49:1394–1401. Med. would have a major risk factors of a Brain Stroke. 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 Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. x = df. Neurol. Stroke is the second leading cause of death worldwide. -H. I. J. Nielsen A, Hansen MB, Tietze A, Mouridsen K. Machine Learning techniques including Random Forest, KNN , XGBoost , Catboost and Naive Bayes have been used for prediction. ijert. The utilization of A bibliometric analysis showed that most studies have focused on using machine learning to improve stroke risk prediction, diagnosis, and outcome prediction 14. May 22, 2023 · The proposed framework, which includes global and local explainable methodologies, can aid in standardizing complicated models and gaining insight into their decision-making, which can enhance stroke care and treatment. . Signal Process. In addition to conventional stroke prediction, Li et al. During the past few decades, several studies were conducted on the improvement of stroke diagnosis using ML in terms of accuracy and speed. According to the performance test, weighted voting 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. Nov 1, 2022 · We use machine learning and neural networks in the proposed approach. It is a big worldwide threat with serious health and economic implications. Jul 1, 2022 · 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 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 Oct 4, 2024 · Lin, C. Predictive modelling and identification of key risk factors for stroke using machine learning. org; NTASU - 2020 Conference Proceedings Pooja Anbuselvan, “Heart Disease Prediction using Machine Learning Techniques Apr 27, 2023 · Use case implementation of LSTM Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. Machine Learning Based Approach Using XGboost for Heart Stroke Prediction. 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. 1093/jamia/ocab068 View in Scopus Google Scholar 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. We used MRI scan data obtained from OpenNeuro, specifically images showing the signs of pre Feb 24, 2023 · Without oxygen, the affected brain cells are starved of oxygen and stop functioning normally. Face to this 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. ˛e proposed model achieves an accuracy of 95. Article Google Scholar Nguyen, L. Biomed. 761 of actual strokes (recall), however it only achieved a Mar 20, 2023 · Building a step wise step Machine Learning Mode. 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 Stroke is one of the main cause of disability across the world. 2, PP. Hence, this work is proposed to perform an empirical analysis and to investigate machine Feb 10, 2021 · Ferdib-Al-Islam and M. 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. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various Nov 7, 2024 · When assessing 1-year stroke prediction as the two scores were designed for, the CHADS 2 risk score was able to correctly classify 0. Study outcome and discussion are in the results and discussion section. 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 Jul 1, 2023 · Dhillon S, Bansal C, Sidhu B. Bachelor of Technology . Aim is to create an application with a user-friendly interface which is easy to navigate and enter inputs. 2021;8:6‐9. 5 million Chinese adults, Journal of the American Medical Informatics Association Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. 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 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 May 20, 2024 · A predictive analytics approach for stroke prediction using machine learning and neural networks. ELATED R WORK Machine Learning has been used successfully in predicting several diseases, like Diabetes [4] and heart disease [5]. Jun 9, 2021 · A model using data science and machine learning was created by Rodrí guez [8] for stroke prediction. Dec 1, 2022 · Brain Stroke Prediction by Using Machine Learning . KADAM1, PRIYANKA AGARWAL2, NISHTHA3, MUDIT KHANDELWAL4 Harshitha KV, Harshitha P, Gupta G, Vaishak P, Prajna KB. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods. 31-43, 2022 Dec 13, 2024 · Stroke prediction is a vital research area due to its significant implications for public health. 2018;15: 1953–1959. We systematically Dec 5, 2021 · Methods. 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 Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. It discusses problems with current diagnosis methods and the need for an automated system. Natural language processing (NLP), statistical analysis, and model-based Stroke is a dangerous, life-threatening brain disorder akin to heart attack, which affects the heart. This research investigates the application of robust machine learning (ML) algorithms, including Brain Stroke Prediction Using Machine Learning Approach DR. Ghosh, “An Enhanced Stroke Prediction Scheme Using Smote and Machine Learning techniques,” 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Jul. 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. Early and precise prediction is crucial to providing effective preventive healthcare interventions. In this paper, we present an advanced stroke detection algorithm Nov 21, 2024 · This document proposes using machine learning algorithms to predict heart disease at early stages. 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. It consists of several components, including data preprocessing, feature extraction, machine learning model training, and prediction. 7% respectively. [Google Scholar] 5. drop(['stroke'], axis=1) y = df['stroke'] 12. The datasets used are classified in terms of 12 parameters like hypertension, heart disease, BMI, smoking status, etc. A variety of data mining techniques are employed in the health care industry to aid in diagnosing and early detection of illnesses. We evaluated models for stroke risk at varying intervals of follow-up (<9 years, 0–3 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. Tan et al. Therefore, we Prediction of stroke is a time consuming and tedious for doctors. Comput. Keywords: Stroke, Thrombolysis, Prediction, Machine learning, Imaging Jun 22, 2021 · For example, Yu et al. 10. 2 METHODS Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. 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. Prediction of Stroke Using Machine Learning KUNDER AKASH MAHESH Dept. 2022;12(10):2392. 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. This repository is a comprehensive project focusing on the prediction of strokes using machine learning techniques. 892 in one cohort analysis. Feb 1, 2025 · This paper describes a thorough investigation of stroke prediction using various machine learning methods. 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. g. An early intervention and prediction could prevent the occurrence of stroke. The prediction of stroke using machine learning algorithms has been studied extensively. 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 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. 2811471 [Google Scholar] 13. IEEE/ACM Trans Comput Biol Bioinform. 2018. The paper compares different machine learning models for stroke prediction and finds that AdaBoost, XGBoost and Random Forest Classifier have the highest accuracy. They experimentally verified an accuracy of more than This Research Topic focuses on recent advances in the design and implementation of machine learning-enabled clinical decision support systems in stroke diagnosis and outcome prediction using real-world datasets, including but not limited to quality data, claims data, electronic health record data, data from wearable technologies. In deeper detail, in [4] stroke prediction was performed on the Cardiovascular Health Study (CHS) dataset. The individual characteristics of patients including clinical data and demographic data were 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. A Mini project report submitted in. Strokes are very common. 1109/TCBB. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. Methods— This Brain stroke prediction using machine learning machine-learning logistic-regression beginner-friendly decision-tree-classifier kaggle-dataset random-forest-classifier knn-classifier commented introduction-to-machine-learning xgboost-classifier brain-stroke brain-stroke-prediction Explore and run machine learning code with Kaggle Notebooks | Using data from National Health and Nutrition Examination Survey Stroke Prediction Using Machine Learning | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Dec 10, 2022 · Brain Stroke is considered as the second most common cause of death. 2021, DOI: 10. 828–0. 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 The brain is the most complex organ in the human body. The application provides a user-friendly dashboard where the user can input symptoms, and the system will process the data to generate a pie Apr 12, 2023 · Early efforts to develop ML algorithms for predicting stroke risk in AF patients have shown some promise, and have achieved an AUC as high as 0. In the data preprocessing module, the This paper proposed Stroke prediction analysis using a machine learning algorithm using a healthcare dataset, including various kinds of risk factors. Monteiro M, Fonseca AC, Freitas AT, Pinho E Melo T, Francisco AP, Ferro JM, et al. The partial fulfilment of the requirements f or the a ward of the degree of. An application of ML and Deep Learning in Nov 14, 2024 · An explainable machine learning pipeline for stroke prediction on imbalanced data. doi: 10. 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. Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke patient survival based on a comprehensive set of factors. 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 . Apr 1, 2023 · Stroke Prediction Using Machine Learning,” in Internationa l Conference on Multi-disciplin ary Trends in Artif icial Intelligence , 2019, pp. The paper reviews 12 studies on machine learning for stroke prediction, focusing on techniques, datasets, models, performance, and limitations. 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. , stroke occurrence), since, in many cases, until all clinical symptoms are manifested and experts can make a definitive diagnosis, the results are essentially irreversible. Stroke is one of the fatal brain diseases that cause death in 3 to 10 hours. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. 5 million per year, it ranks as the second leading cause of death globally. Currently, there is no effective method to predict a stroke using warning signs and hereditary factors. Nov 19, 2023 · The proposed work aims to develop a model for brain stroke prediction using MRI images based on deep learning and machine learning algorithms. Keywords: machine learning, artificial intelligence, deep learning, stroke diagnosis, stroke prognosis, stroke outcome prediction, machine learning in medical imaging 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. We report our results on a balanced dataset created via sub-sampling techniques. 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. 5 million Chinese adults J. This paper describes a thorough investigation of stroke prediction using various machine learning methods. The rest of the paper is organized as follows. Dec 1, 2021 · This document summarizes a student project on stroke prediction using machine learning algorithms. II. Our contribution can help predict Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. However, there are limited works on recurrent stroke prediction using machine learning methods. The rest of the paper is organized as tracks: the methodology is stated in the next section. Stroke prediction using machine learning algorithms. Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry. 019740. 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. 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 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. upr ulmqy fyjups cfoxzm umz dnnqzdk sfpm veiv jfi zgsea vewgx iya iuv asxjog sogol