Brain stroke prediction using machine learning ppt. Very less works have been performed on Brain stroke.

Brain stroke prediction using machine learning ppt predicting the occurrence of a stroke can be made using Machine Learning. Healthcare is a sector The organ known as the brain, which is securely protected within the skull and consists of three main parts, namely the cerebrum, cerebellum, and brainstem, is an incredibly complex and intriguing component of the human body. Stroke causes the unpredictable death and damage to multiple body components. In addition to conventional stroke prediction, Li et al. Reason for topic Strokes are a life threatening condition caused by blood clots in the brain, and the likelihood of these blood clots can increase based on an individual's overall health and lifestyle. This research focuses on predicting brain stroke using machine learning (ML) and Explainable Artificial Intelligence (XAI). AMOL K. As a result, early detection is crucial for more effective therapy. Dataset The dataset used in this project contains information about various health parameters of individuals, including: Apr 26, 2024 · Brain Tumor Detection Using Deep Learning ppt new made. 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 Sep 2, 2011 · The study aims to determine if machine learning can provide accurate predictions of recovery and identify which areas of the brain images inform the predictions. Firstly, the authors in applied four machine learning algorithms, such as naive Bayes, J48, K-nearest neighbor and random forest, in order to detect accurately a stroke. The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" Mehta, Adhikari, and Sharma are the authors. It discusses algorithms like decision trees, XGBoost and SVM that will be used to classify students into suitable career paths based on their academic performance, skills and other attributes. When the supply of blood and other nutrients to the brain is interrupted, symptoms Apr 12, 2024 · Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. 5 approach, Principal Component Analysis, Artificial Neural Networks, and Support Vector Machine. : MDProbability of stroke: a risk profile from the Framingham study. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. RELATED MACHINE LEARNING APPROACHES In this section, analysis and review is being done on the previously published papers related to work on prediction of stroke types using different machine learning approaches. ARUNA VARANASI3, ADIMALLA PAVAN KUMAR4, BILLA CHANDRA KIRAN5, V. The study used a decision tree algorithm and the Cleveland heart disease dataset to train a model. , et al. European Journal of Electrical Engineering and Computer Science, 7(1 Stroke occurs when our brain's blood flow is stopped or reduced, restricting brain tissue from receiving oxygen and important nutrients. 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. Nov 2, 2023 · This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open . The authors examine would have a major risk factors of a Brain Stroke. Nov 2, 2023 · About 18 million people die every year due to cardio vascular diseases (CVDs) such as heart stroke and heart attack. Althaf Rahaman 1 PG Student, 2Assistant Professor 1 Department of Computer Science, 1GITAM (Deemed to be University), Visakhapatnam, India Abstract: A Stroke is a medical disorder that damages the brain by rupturing blood vessels. Brain stroke segmentation in magnetic resonance imaging (MRI) has become an evolving research area in the field of a medical imaging system. Computer aided diagnosis model for brain stroke classification in MRI images using machine learning algorithms. It is the world’s second prevalent disease and can be fatal if it is not treated on time. Stroke, a cerebrovascular disease, is one of the major causes of death. The accuracy of the naive Bayes classifier was 85. Manikandan S. To get the best results, the authors combined the Decision Tree with the C4. The key points are: 1. Dec 5, 2021 · Methods. The objective is to create a user-friendly application to predict stroke risk by entering patient data. P [3], Elamugilan. This study aimed to address some of the limitations of previous studies by Nov 1, 2022 · Hung et al. Jun 21, 2022 · 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. A stroke is generally a consequence of a poor Keywords—Accuracy, Data preprocessing, Machine Learning, Prediction,Stroke I. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms. Seeking medical help right away can help prevent brain damage and other complications. Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. pptx), PDF File (. In recent times, stroke can be often seen Mar 4, 2022 · Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. Face to this Machine learning algorithms have shown promising potential in predicting stroke occurrences based on various risk factors. The models obtained from this research are just a A brain stroke happens when blood flow to a part of the brain is interrupted or reduced. 02% using LSTM. 6% made using Machine Learning. NeuroImage Clin. If a stroke is identified early enough, it is possible to receive the appropriate therapy and recover from the stroke. pdf), Text File (. B. Keywords - Machine learning, Brain Stroke. In this paper, we present an advanced stroke detection algorithm 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. S. It can also happen when the Stroke is a leading cause of disability and death worldwide, often resulting from the sudden disruption of blood supply to the brain. Methods— This Dec 31, 2024 · Prediction of brain stroke using machine learning algorithms and deep neural network techniques. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Stroke, a condition that ranks as the second leading cause of death worldwide, necessitates immediate treatment in order to prevent any potential damage to the brain. Prediction of stroke is a time consuming and tedious for doctors. Apr 27, 2023 · This document presents a project that aims to predict the chances of stroke occurrence using machine learning techniques. 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: (i) Random forest (ii) Decision tree (iii) The brain stroke prediction module using machine learning aims to predict the likelihood of a stroke based on input data. Stroke 22(3), 312–318 (1991) Google Scholar A stroke, also known as a cerebrovascular accident or CVA is when part of the brain loses its blood supply and the part of the body that the blood-deprived brain cells control stops working. As a result, this research work attempts to develop a stroke prediction system to assist doctors and clinical workers in predicting strokes in a timely and efficient manner. 7) Oct 12, 2022 · In this study, we develop a machine learning algorithm for the prediction of stroke in the brain and this prediction is carried out from the real-time samples of electromyography (EMG) data as illustrated in Figure 3. At least, papers from the past decade have been considered for the review. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. It is a big worldwide threat with serious health and economic implications. Therefore, the aim of Nov 29, 2024 · This document describes a study that developed a machine learning model to predict heart disease risk and provide recommendations. - lekh-ai/Brain-Stroke-Research This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. 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. An application of ML and Deep Learning in Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. [Google Scholar] Associated Data Mar 23, 2022 · The concern of brain stroke increases rapidly in young age groups daily. Machine learning (ML) techniques have gained prominence in recent years for their potential to improve healthcare outcomes, including the prediction and prevention of stroke. Ischemic Stroke, transient ischemic attack. In this experiment, we implement a process of stroke risk prediction Jun 3, 2023 · Bentley, P. It primarily occurs when the brain's blood supply is disrupted by blood clots, blocking blood flow, or when blood vessels rupture, causing bleeding and damage to brain tissue. Various data mining techniques are used in the healthcare industry to 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. Read less Early Prediction of Brain Stroke Using Machine Learning Kalaiselvi. I. A [4], Prasanth. This 6364e8cketans Ppt Stroke Prediction - Free download as Powerpoint Presentation (. Topics In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. The number of people at risk for stroke 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 6 days ago · Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. This report explores the use of Machine Learning (ML) techniques to predict the likelihood of stroke based on patient health data. Mar 30, 2019 · Methodology • Learning Algorithms for Prediction • Margin-based Censored Regression SVM Experiments • Data Imputation • Feature Selection Experiments • Stroke Prediction Using machine learning algorithms to analyze patient data and identify key factors contributing to stroke occurrences. Nov 21, 2024 · This document discusses the use of machine learning techniques for detecting brain strokes using MRI scans. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. INTRODUCTION When a blood vessel bleed or blockage lowers or stops the flow of blood to the brain, a stroke ensues. 85% and a deep learning accuracy of 98. Dec 1, 2022 · Stroke is one of the most serious diseases worldwide, directly or indirectly responsible for a significant number of deaths. HRITHIK REDDY6 1, 2 Assistant Professor, Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Telangana. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. P [1], Vasanth. This causes the brain to receive less oxygen and nutrients, which damages brain cells begin to deteriorate. It's a medical emergency; therefore getting help as soon as possible is critical. The proposed machine Jan 7, 2024 · # Plus, we won't use the id column, so we can drop it. May 13, 2023 · This document summarizes a student project on stroke prediction using machine learning algorithms. 7% respectively. Oct 1, 2020 · Machine learning techniques for brain stroke prognostic or outcome prediction. This study proposes an accurate predictive model for identifying stroke risk factors. Machine learning algorithms are The situation when the blood circulation of some areas of brain cut of is known as brain stroke. Stroke, a leading neurological disorder worldwide, is responsible for over 12. 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. MAMATHA2, DR. 2020;29(5):7976–7990. It consists of several components, including data preprocessing, feature extraction, machine learning model training, and prediction. So, the prediction of stroke is significant for early intervention and treatment. 03 Billion in 2016 to USD 8. Jan 20, 2023 · Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. It's much more monumental to diagnostic the brain stroke or not for doctor, but the main A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. The system uses image processing and machine learning techniques to identify and classify stroke regions within the brain, aiming to provide early diagnosis and assist medical professionals in treatment planning. : Prediction of stroke thrombolysis outcome using CT brain machine learning. The works previously performed on stroke mostly include the ones on Heart stroke prediction. The algorithms present in Machine Learning are constructive in making an accurate prediction and give correct analysis. View Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. In this thorough analysis, the use of machine learning methods for stroke prediction is covered. ppt / . 5 million. Five different algorithms are used and compared to achieve better accuracy. Every year, more than 15 million people worldwide have a stroke, and in every 4 minutes, someone dies due to stroke. There have lots of reasons for brain stroke, for instance, unusual blood circulation across the brain. Implementing a combination of statistical and machine-learning techniques, we explored how Jun 25, 2020 · 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. Out of all CVDs, the stroke was considered as the dangerous disease as it is directly linked to the brain. Stroke can be predicted by analyzing different warning signs. This study presents a new machine learning method for detecting brain strokes using patient information. An ML model for predicting stroke using the machine The data used in this project are available online in educational purpose use. May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. Note: Machine Learning (ML), Computerized Tomography (CT), Area Under receiver-operating-characteristic Curve (AUC), Artificial Neural Network (ANN) and Support Vector Machine (SVM), Residual Neural Network (ResNet), Structured Receptive Fields (RFNN), auto-encoders The situation when the blood circulation of some areas of brain cut of is known as brain stroke. 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]. Nov 29, 2024 · The document describes a proposed intelligent career guidance system using machine learning. This research present the detection and segmentation of brain stroke using fuzzy c-means clustering and H2O deep learning algorithms. This project describes step-by-step procedure for building a machine learning (ML) model for stroke prediction and for analysing which features are most useful for the prediction. The study uses synthetic samples for training the support vector machine (SVM) classifier, and then, the testing is conducted in Oct 1, 2024 · 1 INTRODUCTION. International Journal of Advanced Science and Technology . There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke Keywords: cerebrovascular disease, deep learning, machine learning, reinforcement learning, stroke, stroke therapy, supervised learning, unsupervised learning Introduction Stroke is one of the most common and devastating disorders, a leading cause of disability, and the second leading cause of death worldwide cause, with approximately 5. Mar 8, 2024 · This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. It's much more monumental to diagnostic the brain stroke or not for doctor, but the main Brain Stroke Prediction Using Machine Learning Approach DR. The primary objective of this study is to develop and validate a robust ML model for the prediction and early detection of stroke in the brain. 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. We employed six [4] “Prediction of stroke thrombolysis outcome using CT brain machine learning” - Paul Bentley, JebanGanesalingam, AnomaLalani, CarltonJones, KateMahady, SarahEpton, PaulRinne, PankajSharma, OmidHalse, AmrishMehta, DanielRueckert - Clinical records and CT brains of 116 acute ischemic stroke patients 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. Results indicate that while random forest achieves high accuracy, logistic regression provides a balanced sensitivity-specificity trade-off. S [5] Department of Artificial Intelligence and Data Science, Sri Sairam Engineering College - Chennai ABSTRACT Brain stroke is one of the driving causes of death and disability worldwide. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. in [18] used machine learning approaches for predicting ischaemic stroke and thromboembolism in atrial fibrillation. The May 23, 2024 · Ismail and Materwala analyzed stroke data under an intelligent stroke prediction framework and compared five common machine learning algorithms: decision tree, random forest, support vector machine, naive Bayes, and logistic regression; and random forest gave the best results on the stroke test data set. stroke_df. 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. The leading causes of death from stroke globally will rise to 6. ” 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. , Dhanalakshmi P. The main objective of this study is to forecast the possibility of a brain stroke occurring at Five machine learning techniques were applied to the Cardiovascular Health Study (CHS) dataset to forecast strokes. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. drop('id', axis=1, inplace=True) # Filling the NaN Values # For the NaN Values, there are various methods that can be applied, we Sep 26, 2024 · This document discusses decision tree regression for predicting salary based on position level. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome BRAIN STROKE PREDICTION USING SUPERVISED MACHINE LEARNING 1 Kallam Bhavishya, 2Shaik. So, it is imperative to create a novel ML model that can optimize the performance of brain stroke prediction. In this research work, with the aid of machine learning (ML 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. This system can aid in the effective design of sentiment analysis systems in Bangla. It provides an overview of machine learning and its applications in neuroimaging and brain stroke detection. Oct 18, 2023 · Brain Stroke Prediction Machine Learning. Using a machine learning algorithm to predict whether an individual is at high risk for a stroke, based on factors such as age, BMI, and occupation. The dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. txt) or view presentation slides online. The students collected two datasets on stroke from Kaggle, one benchmark and one non-benchmark. After the stroke, the damaged area of the brain will not operate normally. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Dec 1, 2021 · The document summarizes a disease prediction system for rural health services presented by two students. et al. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods. The predictions resulting from this model can save many lives or give people hints on how they can protect themselves from the risk. They are explained below: Declaration We hereby declare that the project work entitled “Brain Stroke Prediction by Using Machine Learning” submitted to the JNTU Kakinada is a record of an original work done References [1] Manish Sirsat Eduardo Ferme, Joana Camara, “Machine Learning for Brain stroke: A Review, ” Journal of stroke and cerebrovascular disease: the official journal of National Stroke Association(JSTROKECEREBROVASDIS), 20220 [2] Harish Kamal, Victor Lopez, Sunil A. 2. In this study, we propose the utilization of Random Forest and AdaBoost algorithms for brain stroke prediction The goal of this study is to develop a brain stroke prediction model using the Random Jul 1, 2019 · To detect the relationship between potential factors and the risk of stroke and examine which machine learning method significantly can enhance the prediction accuracy of stroke. 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. 5 The algorithms present in Machine Learning are constructive in making an accurate prediction and give correct analysis. In the data preprocessing module, the Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Mar 15, 2024 · SLIDESMANIA ConcluSion Findings: Through the use of AI and machine learning algorithms, we have successfully developed a brain stroke prediction model. 4, 635–640 (2014) Google Scholar Philip, A. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. stroke at its early stage. In this section, we will present the latest works that utilize machine learning techniques for stroke risk prediction. The workspreviously performed on stroke mostly include the ones on Heart stroke prediction. Very less works have been performed on Brain stroke. Nowadays, it is a very common disease and the number of patients who attack by brain stroke is skyrocketed. 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 document provides background on strokes, machine learning applications to neuroimaging, and describes the data acquisition and testing methodology used in the study. 2 million new cases each year. The model has been trained using a comprehensive dataset and has shown promising results in accurately predicting the likelihood of a brain stroke. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. pptx - Download as a PDF or view online for free Oct 18, 2023 · Brain Stroke Prediction Machine Learning. Feb 23, 2024 · The research contributes to the growing literature on machine learning applications in healthcare by presenting a holistic approach to stroke prediction. We employ a comprehensive dataset featuring This project aims to predict the likelihood of a person having a brain stroke using machine learning techniques. This paper is based on predicting the occurrenceof a brain stroke using Machine Brain Stroke Prediction Using Machine Learning and Data Science VEMULA GEETA1, T. in [17] compared deep learning models and machine learning models for stroke prediction from electronic medical claims database. An early intervention and prediction could prevent the occurrence of stroke. 1% during the forecast period. Methods— This Mar 2, 2024 · Brain stroke is a Cerebrovascular accident that is considered as one of the threatening diseases. 2% and precision of 96. 81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44. This paper is based on predicting the occurrence of Dec 16, 2022 · Our approach yields a machine learning accuracy of 65. published in the 2021 issue of Journal of Medical Systems. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. G [2], Aravinth. It causes significant health and financial burdens for both patients and health care systems. Sheth, “Machin e Learning in Acute Ischemic Stroke Neuroimaging, ” Frontiers in Neurology (FNEUR) 2018. Five Jun 25, 2021 · This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. 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 A stroke occurs when the blood supply to a person's brain is interrupted or reduced. In this work, we compare different methods with our approach for stroke Nov 24, 2022 · Based on machine learning, this paper aims to build a supervised model that can predict the presence of a stroke in the near future based on certain factors using different machine learning classification methods. KADAM1, PRIYANKA AGARWAL2, Brain Stroke Prediction Using Machine Learning Approach Author: Nov 26, 2021 · The most common disease identified in the medical field is stroke, which is on the rise year after year. It is a critical medical condition that demands timely detection to prevent severe outcomes, including permanent paralysis and death. Dependencies Python (v3. 7 million yearly if untreated and undetected by early The brain is the most complex organ in the human body. 1-3 Deprivation of cells from oxygen and other nutrients during a stroke leads to the death of A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. In any of these cases, the brain becomes damaged or dies. Worldwide, it is the second major reason for deaths with an annual mortality rate of 5. The prediction model takes into account Mar 15, 2024 · The talk covers traditional machine learning versus deep learning, using deep convolutional neural networks (DCNNs) for image analysis, transfer learning and fine-tuning DCNNs, recurrent neural networks (RNNs), and case studies applying these techniques to diabetic retinopathy prediction and fashion image caption generation. Keywords: Stroke, Thrombolysis, Prediction, Machine learning, Imaging 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]. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. 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 prediction of stroke using machine learning algorithms has been studied extensively. The results of several laboratory tests are correlated with stroke. Machine learning applications are becoming more widely used in the health care sector. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Treatment of stroke disease is very crucial. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. However, no previous work has explored the prediction of stroke using lab tests. 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. qhpgz qtojr sbprqb obkrnhg mhgdt isfxut iskaom gcmgs yjn rlwv elbp vjzlxsk tnony rft yataxf