Stroke prediction using deep learning in [18] used machine learning approaches for predicting ischaemic stroke and thromboembolism in atrial brillation. In the medical industry, the occurrence of a stroke can be easily predicted using Machine Learning algorithms [6] [7]. 019740. Brain strokes, a major public health concern around the world, necessitate accurate and prompt prediction in order to reduce their devastation. The proposed work aims at designing a model for stroke prediction from Magnetic resonance images (MRI) using deep learning (DL) techniques. 1136/neurintsurg-2017-013355 [Google Scholar] 26. View Show abstract Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. . This project utilizes Python, TensorFlow, or PyTorch, along with medical imaging datasets specific to brain images. The number of people at risk for stroke Nov 27, 2024 · First, we aim to demonstrate how Federated Learning can enhance stroke detection and prediction using Deep Learning, compared with other approaches. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Age, heart disease, average glucose level are important factors for predicting stroke. The aim of this study is to compare these Nov 1, 2022 · We propose a predictive analytics approach for stroke prediction. Deep learning is capable of constructing a nonlinear Dec 2, 2024 · This study aims to investigate the use of deep learning techniques for predicting ischemic strokes with high accuracy, enabling earlier diagnosis and intervention. This paper focuses on developing a prediction model for Jun 26, 2024 · Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. , ECG). In addition to conventional stroke prediction, Li et al. Early detection is crucial for effective treatment. </p Discover the world's research 25 Jan 1, 2024 · This paper’s following sections are structured as follows: a literature review of the methods for treating stroke diseases using EEG and ML was presented in Section 2. 381 - 388 , 10. 10. Building a real AI for mobile AI in an 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. Using multi-modal bio-signals, such as electrocardiogram (ECG) and 1. Various deep learning (ML) algorithms such as CNN, Densen et and VGG16 are used in this study. Front. Jan 10, 2025 · Early stroke detection is essential for effective treatment and prevention of long-term disability. Contribute to ratan54/Stroke-Prediction-Using-Deep-learning development by creating an account on GitHub. Feb 1, 2023 · Deep learning-based stroke disease prediction system using real-time bio signals. 1161/STROKEAHA. As a result, early detection is crucial for more effective therapy. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. 1: (i) a convolutional neural network (CNN) encoder with shared weights across time to extract high-level spatial features from each time point, (ii) a Jan 24, 2022 · This study aims to investigate post-stroke pneumonia prediction models using more advanced machine learning algorithms, specifically deep learning approaches. Transfer Learning App roaches . Dec 1, 2019 · This study takes the initiative to develop new post-stroke pneumonia prediction models using novel deep learning algorithms, which combine time-insensitive features such as disease history and demographic information with the time series of medications and lab tests for pneumonia prediction. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. Through this study, a strategy for identifying brain stroke disease using deep learning techniques and image preprocessing is provided. May 23, 2024 · In this paper, we investigate a deep neural network-based stroke prediction system using a publicly available data set of stroke to automatically output the prediction results in an end-to-end manner. Most stroke research has centered on MRI and CT scans for uncomplicated categorization. Jun 22, 2021 · Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This study’s goal was to predict ordinal 90-day modified Rankin Dec 16, 2021 · Yu, Y. 10346639 with brain stroke prediction using an ensemble model that combines XGBoost and DNN. Section 4 discusses application of deep learning model on heart disease dataset and the conclusion and future work are presented in section 5 of this paper. 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. In[3] Stroke Risk Prediction with Machine Learning Techniques. Nielsen et al. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning and interpreting the predictive features, as well as, how to effectively combine neuroimaging and tabular Mar 12, 2020 · Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Medical image data is best analysed using models based on Convolutional Neural Networks (CNNs). Deep learning methods have shown promising results in detecting various medical conditions, including stroke. 4269. in this paper LSTM which is a deep learning techniques which is used to obtain the accuracy in the brain stroke prediction . Mar 27, 2023 · Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Stacking. This study provides a comprehensive assessment of the literature on the use of Machine Learning (ML) and . Jun 22, 2021 · The stroke prediction module for the elderly using deep learning-based real-time EEG data proposed in this paper consists of two units, as illustrated in Figure 4. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning, and how to effectively combine neuroimaging and tabular data (e. Among the several medical imaging modalities used for brain imaging Jul 1, 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. 019740  PubMed Google Scholar Crossref Dec 1, 2020 · The use of deep learning models in the prognosis of stroke can greatly benefit the current approach to stroke treatment. Sep 28, 2020 · Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms were used in our system. 2023. INTRODUCTION Stroke, the second leading cause of morbidity and mortal-ity worldwide, occurs due to sudden disruptions in cerebral blood flow that result in neurocellular damage or death [1], [2]. P. Among the four major cardiovascular diseases, stroke is one of the deadliest and potentially fatal, but, if detected early enough, a patient's life may be spared. If left untreated, stroke can lead to death. achieved stroke risk prediction by analyzing facial muscle incoordination and speech impairment in suspected stroke patients [1]. 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. We use machine learning and neural networks in the proposed approach. This paper describes a thorough investigation of stroke prediction using various machine learning methods. Section 5 presents the evaluation model. • Demonstrating the model’s potential in automating Oct 15, 2024 · Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. 7) May 1, 2023 · Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning Stroke , 49 ( 6 ) ( 2018 ) , pp. where P k, c is the prediction or probability of k-th model in class c, where c = {S t r o k e, N o n − S t r o k e}. Methods: The study utilized advanced deep learning algorithms, specifically ConvNeXt Base, to analyze large datasets of medical imaging data, focusing on MRI scans. To fully exploit the potential of deep learning models, it is important to acquire large data sets. Deepak Kumar*1, Sagar Yellaram*2, Sumanth kothamasu*3, SurendharReddy Puchakayala*4 *1Assistant Professor,Department of Computer Science and Engineering, CMR Technical Campus, Medchal, Telangana, India *2JNTUH, Computer Science and Engineering, CMR Technical Campus, Medchal, Telangana, India Dec 26, 2021 · The efficacy of deep learning in stroke diagnosis is gaining This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the BrainStrokePredictionAI is a deep learning project focused on using medical image analysis techniques to predict brain strokes from imaging data. 73% in KNN and 81. , Lin B. Tan et al. 15:1394879. However, there are several drawbacks of using deep learning in stroke diagnosis or prediction of recovery, such as the need for large amounts of data for effective model training, which may be challenging to obtain for rare or specific stroke subtypes . Anand Kumar and others published Stroke Disease Prediction based on ECG Signals using Deep Learning Techniques | Find, read and cite all the research you need on ResearchGate for compute the results of brain stroke for early prediction of disease. Google Scholar Raja MS, Anurag M, Reddy CP, Sirisala NR (2021) Machine learning based heart disease prediction system. J Healthcare Eng. The data was Jan 15, 2024 · It is the main cause of death in the recent research . Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. 3389/fneur. Therefore, the aim of Oct 29, 2017 · The MRI images are preprocessed and then deep learning methods namely DenseNet-121, ResNet-50 and VGG-16 are implemented for the prediction of stroke. (2018) 10:358–62. In this proposed work train and test as per the Model Nov 2, 2023 · Tazin T, Alam MN, Dola NN, Bari MS, Bourouis S, Monirujjaman Khan M (2021) Stroke disease detection and prediction using robust learning approaches. We systematically Keywords— Brain-stroke, Prediction, Deep learning, Convolutional Neural Networks. Timely treatment can improve stroke prognosis. 2019. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Because deep learning is capable of extracting intricate patterns from massive amounts of medical data, it has shown great promise as a tool for predicting stroke illness. ( Elias Dritsas and and Maria Trigka,2022) [3] "Stroke Risk Prediction with Machine Learning Techniques," Elias Dritsas and Maria Trigka propose a methodology for predicting stroke risk using machine learning. Dec 1, 2024 · Soft voting based on weighted average ensemble machine-learning methods for brain stroke prediction utilizing clinical variables gathered from the University of California Irvine Machine Learning Repository(UCI) repository, which has 4981 rows and 11 columns, was proposed in a research study [17]. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. 2018;49(6):1394-1401. For the offline May 28, 2019 · The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Feb 1, 2025 · Eight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. 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. The hybrid deep learning and metaheuristic model is described in detail in Section 4. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. Additionally, the complexity of deep learning models can limit their interpretability An automated early ischemic stroke detection system using CNN deep learning algorithm; Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST); Taichung, Taiwan. -L. 9. 7 of this paper. Methods: Using a hospital's Aug 1, 2023 · Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. They detected strokes using a deep neural network method. 8–10 November 2017; pp. It is deep learning model. 22% in ANN, 80. Received: 02 March 2024; Accepted: 12 Index Terms—stroke segmentation, vision Transformer, convo-lutional neural network, nnU-Net, deep learning I. It will increase to 75 million in the year 2030[1]. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Reddy and others published Brain Stroke Prediction Using Deep Learning: A CNN Approach | Find, read and cite all the research you need on ResearchGate Jan 1, 2024 · Preliminary investigation deep learning in the prediction of ischaemic stroke thrombolysis functional outcomes: A pilot study Academic Radiol , 27 ( 2020 ) , pp. Stroke. 1394 - 1401 Crossref View in Scopus Google Scholar rapid development of deep learning-based machine learning algorithms in recent years, the application of AI in diagnosis, risk stratification, and therapeutic decision-making has become ever- more widespread. 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]. 85 (6), 460–466. 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. pp. Nielsen A, Hansen MB, Tietze A, Mouridsen K. The way the nervous system is Jan 1, 2024 · Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. INTRODUCTION A stroke ensues when blood flow for any part of brain is detached. May 20, 2024 · Future work will focus on analyzing the dataset using deep learning methods to enhance accuracy. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement Jan 14, 2022 · The machine learning- and deep learning-based learning and prediction module proposed in this paper constitutes the following two subblocks (see Fig. The key contributions of this study can be summarized as follows: • Conducting a comprehensive analysis of features in-fluencing brain stroke prediction using the XGBoost-DNN ensemble model. doi: 10. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. The purpose of this paper is to develop an automated early ischemic stroke detection system using CNN deep learning algorithm. This technique employs learning from data with multiple level of abstraction by com- Mar 23, 2022 · Accuracy achieved for Stroke Prediction Dataset using 10 Fold Cross-Validation MLP is classified as a deep learning technique . The MRI images are preferred as it Stroke is one of the main causes of death and disability in the world. Crossref View in Scopus Google Scholar. Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST); November 2017; Taichung, Taiwan. Awareness of stroke warning signs and appropriate actions in the event of a stroke improve outcomes. Jan 24, 2022 · The objective of this research is to apply three current Deep Learning (DL) approaches for 6-month IS outcome predictions, using the openly accessible International Stroke Trial (IST) dataset. Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. demographic information and clinical For the last few decades, machine learning is used to analyze medical dataset. Heart Stroke is one of the severe health hazards; therefore, early heart stroke prediction helps the society to save human lives. 368–372. [7] The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" 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. We identify the most important factors for stroke prediction. Open 3 , e200772–e200772 (2020). Mattas, P. , Wu G. Jul 8, 2018 · Section 4 describes prediction of stroke using EHRs and deep learning. Section 3 explores deep learning-based stroke disease prediction systems with real-time brainwave data proposed in the paper, and also discusses prediction methodologies using raw data and frequency properties of brainwaves. The proposed methodology is to Dec 8, 2022 · A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. These models have been shown to achieve high accuracy in classifying stroke type, and they have the advantage of being capable of learning the features Oct 1, 2020 · Nowadays, stroke is a major health-related challenge [52]. Section 6 discusses the result of this research and the conclusion and future work are present in Sect. In most cases, patients with stroke have been observed to have abnormal bio-signals (i. Dec 28, 2024 · Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. Users may find it challenging to comprehend and interpret the results. Jan 15, 2024 · Authors developed a stroke risk prediction model using a novel Hybrid Deep Transfer Learning-based Stroke Risk Prediction (HDTL-SRP) framework by utilizing the knowledge structure from multiple correlated sources, such as external stroke data and chronic diseases data like hypertension and diabetes 2021 Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient recovers. The performance of deep learning methods is Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. [Google Scholar] 12. Nov 21, 2024 · This document summarizes different methods for predicting stroke risk using a patient's historical medical information. 892 in one cohort analysis. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. g. Better methods for early detection are crucial due to the concerning increase in the number of people suffering from brain stroke. In this research article, machine learning models are applied on well known heart stroke classification data-set. Aug 1, 2024 · The system not only surpasses existing benchmarks in stroke prediction but also paves the way for future research into the broader application of retinal biomarkers along with deep learning methods in medical diagnostics and prognostics. Nov 14, 2022 · Section 3 discusses the applications of deep learning to stroke management in five main areas. Dependencies Python (v3. J Neurointerv Surg. - hernanrazo/stroke-prediction-using-deep-learning 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. 32% in Support Vector Machine. Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. 3. Jun 9, 2021 · Consequently, this work aims to create a computer-based system for the prediction of stroke utilizing deep learning techniques, which help in timely diagnosis. The paper reviews 12 studies on machine learning for stroke prediction, focusing on techniques, datasets, models, performance, and limitations. Jun 25, 2020 · In this work, deep Transfer Learning based Stroke Risk Prediction scheme is proposed to exploit the knowledge structure from multiple correlated sources and used bayesian optimization for Dec 1, 2020 · The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. I. 5 million people dead each year. However, while doctors are analyzing each brain CT image, time is running Jun 22, 2021 · Conclusions— Machine learning algorithms, particularly the deep neural network, can improve the prediction of long-term outcomes in ischemic stroke patients. In summary, our study represents a significant advancement in predictive healthcare. In the Jan 1, 2021 · Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning Stroke , 49 ( 2018 ) , pp. They have 83 percent area under the curve (AUC). Medical service use and health behavior data are easier to collect than medical imaging data. Second, we aim to evaluate the model’s performance, focusing on accuracy and sensitivity. Stroke, also known as cerebrovascular accident, consists of a neurological disease that can result from ischemia or hemorrhage of the brain arteries, and usually leads to heterogeneous motor and cognitive impairments that compromise functionality [34]. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. 8: Prediction of final lesion in Applications of deep learning in acute ischemic stroke imaging analysis. Oct 1, 2020 · Prediction of post-stroke pneumonia in the stroke population in China [26] LR, SVM, XGBoost, MLP and RNN (i. YOLO5 and SSD models together was successful in achieving high levels of accuracy . They proposed a multimodal deep learning framework based on transfer learning. The authors utilized PCA to extract information from the medical records and predict strokes. Nov 26, 2021 · They identified the stroke incidence using 15,099 individuals in their research. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. Treatment and diagnosis must begin early in order to improve patient outcomes. Citation: Yang Y and Guo Y (2024) Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network. , 2018 Deep learning guided stroke management: a review of clinical applications. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. In today's era, the convergence of modern technology and healthcare has paved the path for novel diseases prediction and prevention technologies. However, it is not clear which modality is superior for this task. Hung et al. (2018) 49:1394–401. In Section 3, a cloud-based decision support system for stroke diagnosis is described. DONG-HER SHIH 1, YI-HUEI WU 2, T ING-WEI WU 3, HUEI-YING CHU 4, an d MING-HUNG SHIH 5. Here, we used a deep neural Feb 5, 2025 · The goal for this challenge is to predict a binary mask of the final infarct using acute 4D CTP imaging data. After the stroke, the damaged area of the brain will not operate normally. Their approach likely involves leveraging diverse datasets and employing 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 Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischemic stroke patients. Sensors, 21 (13) (2021), p. , 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? Implementation of the study: "The Use of Deep Learning to Predict Stroke Patient Mortality" by Cheon et al. This paper provides a comprehensive review of recent advancements in the use of deep learning for stroke lesion segmentation in both MRI and CT scans. Aug 1, 2022 · Studies on stroke risk prediction use data sets collected by non-medical equipment. in [17] compared deep learning models and machine learning models for stroke prediction from electronic medical claims database. Achieved an accuracy of 82. Building upon our previous work [], we applied and tested a model architecture consisting of three modules, as shown in Fig. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. The study shows how CNNs can be used to diagnose strokes. Stroke Prediction Using Machine Learning (Classification use case) Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. For example, Tongan Cai et al. Early recognition and detection of symptoms can aid in the rapid treatment of Jun 22, 2021 · The stroke prediction module for the elderly using deep learning-based real-time EEG data proposed in this paper consists of two units, as illustrated in Figure 4 . since it uses multiple layers of neurons. This medical Oct 4, 2024 · In addition, the authors investigated 20 the use of predictive analytics techniques for stroke prediction using deep learning models applied to heart disease datasets. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. To implement the results of brain stroke various machine learning classifier were employed such as RF ,LR, DT, KNN and LSTM is used to improve the accuracy in the 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]. Brain stroke prediction using machine learning. Int. Then, deep learning models were used to predict whether the patients would have a stroke. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. 3. An automated early ischemic stroke detection system using CNN deep learning algorithm. These models have been applied to brain scans, including magnetic resonance imaging (MRI) and May 3, 2024 · Keywords: acute ischemic stroke, outcome prediction, whole brain, deep learning, machine learning. Oct 1, 2023 · One more approach is to use deep learning (DL) methods, like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to classify brain strokes directly from imaging data. , et al. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. This research design uses one of the following algorithms that can predict beats and provide new insights with accuracy. For the offline processing unit, the EEG data are extracted from a database storing the data on various biological signals such as EEG, ECG, and EMG, and a series of processes are May 15, 2024 · When it comes to finding solutions to issues, deep learning models are pretty much everywhere. In addition, three models for predicting the outcomes have been developed. The complex There was a great category imbalance between stroke and non-stroke patients, so this study tried to use various techniques to solve the problem of categorical unbalanced stroke prediction problem. -J. EMG (Electromyography) bio-signals were collected in real time from thighs and Jul 2, 2024 · for enhancing CT image quality to aid in stroke prediction through deep learning analysis. DL approaches and Chin C. Yu et al. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. SPEM employs morphological erosion to reduce noise and simplify raw CT images, en-hancing visibility for In this paper, we investigate a deep neural network-based stroke prediction system using a publicly available data set of stroke to automatically output the prediction results in an end-to-end manner. 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 Heart Stroke Prediction using Machine Learning B. Dec 1, 2023 · In recent years, deep learning-based approaches have shown great potential for brain stroke segmentation in both MRI and CT scans. Healthcare professionals can discover Jul 31, 2024 · The application of noninvasive methods to enhance healthcare systems has been facilitated by the development of new technology. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. Prediction of brain stroke using clin-ical attributes is prone to errors and takes lot of time. Finally, we present outlook in Section 4. We aimed to examine the performance of machine learning–based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality. Prediction of brain stroke in the Sep 24, 2023 · Prediction of motor outcome of stroke patients using a deep learning algorithm with brain MRI as input data. 7% respectively. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. 22% in Logistic Regression, 72. JAMA Netw. 2. 2024. For example, in [47], the authors developed a pre-detection and prediction technique using machine learning and deep learning-based approaches that measured the electrical activity of thighs and calves with EMG biological signal sensors. 1109/ICCCMLA58983. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small There is a subfield of neural networks called Deep Learning (DL), which uses more than three layers—more than one hidden layer—of neural networks. May 20, 2022 · PDF | On May 20, 2022, M. Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. 2% and precision of 96. 1159/000525222 Jul 24, 2023 · BACKGROUND: Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. There was an imbalance in the dataset. The input variables are both numerical and categorical and will be explained below. based on deep learning. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. ML and Deep Dec 15, 2022 · State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. S. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. PubMed Abstract | CrossRef Full Text | Google Scholar Oct 29, 2017 · The result of Naïve Bayes and SVM show that patients are suffering from stroke or not deep learning technique shows in percentage of a chance of stroke. The MRI images are preprocessed and then Oct 11, 2023 · E ective Brain Stroke Prediction with Deep Learning Model by Incorporating Y OLO_5 and SSD. Stroke is a common cause of mortality among older people. Sep 21, 2022 · PDF | On Sep 21, 2022, Madhavi K. Furthermore, another objective of this research is to compare these DL approaches with machine learning (ML) for performing in clinical prediction. MLP is . 1016/j. 015 Oct 27, 2020 · The brain is an energy-consuming organ that heavily relies on the heart for energy supply. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. Prediction of final infarct volume: CNN: Deep convolutional neural network accurately predicts final lesion volume in acute ischemic stroke, enhancing personalized treatment planning. Brain cells die and the Jan 1, 2024 · Stroke Prediction Using Deep Learning and . This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. Apr 18, 2023 · A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Oct 29, 2023 · Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Jul 2, 2024 · Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain’s blood flow, often caused by blood clots or artery blockages. -R.  Stroke . It is a big worldwide threat with serious health and economic implications. e19 - e23 , 10. In Section 2, we exhibit the historical development of deep learning, including convolutional neural network (CNN), recurrent neural network (RNN), autoencoder (AE), restricted Boltzmann machine (RBM), transformer, and transfer learning (TL). Deep learning (DL), derived from artificial neural networks (ANNs), mimics human brain intelligence in increasingly sophisticated and independent ways . Eur. Stroke Prediction Using Deep Learning. 4): first, in the batch processing block, machine learning and deep learning are performed by storing and preprocessing motion data collected in real time to extract important attributes. 2 Deep Learning Recently researchers [8-10] have been using deep learning technique for prediction. doi:10. Nov 26, 2021 · The dataset used in the development of the method was the open-access Stroke Prediction dataset. Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can Dec 1, 2021 · According to recent survey by WHO organisation 17. Explainable AI (XAI) can explain the Jul 4, 2024 · We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. The rest of this paper is organized as follows. 03. Aug 1, 2024 · Developing a deep learning heart stroke prediction model using combination of fixed row initial centroid method with navie Bayes and decision tree classifiers 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) ( 2023 ) , pp. INTRODUCTION Now-a-days brain stroke has become a major Stroke that is leading to death. , attention based GRU) 13,930: EHR data: within 7 days of post-stroke by GRU: AUC= 0. Jun 11, 2021 · Stroke has become a leading cause of death and long-term disability in the world with no effective treatment. Jun 1, 2018 · Background and Purpose—Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. et al. Jan 1, 2022 · prediction by using various machine learning algorithms including Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Cla ssification, and Jan 1, 2024 · Prediction of stroke diseases has been explored using a wide range of biological signals. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Publ. e. 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. In this research work, with the aid of machine learning (ML Jan 4, 2024 · The outcomes of the proposed approach for stroke prediction in IOT healthcare systems show that improved performance is attained using deep learning methods. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction of the major causes of mortality worldwide. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final Jan 15, 2023 · In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. This confirmed that deep learning technique is most suitable for generating the heart dataset for predictive analysis in stroke. 1394 - 1401 Crossref View in Scopus Google Scholar Developed a deep learning model to detect heart stroke using artificial neural networks and various other algorithms and using Keras. Deep learning is widely used in prediction of diseases This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. The accuracy percentage of the models used in this investigation is significantly higher than that May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. With Dec 5, 2021 · 26. Oct 3, 2023 · The combination of big data and deep learning is a world-shattering technology that can greatly impact any objective if used properly. By using a collection of brain imaging scans to train CNN models, the authors are able to accurately distinguish between hemorrhagic and ischemic strokes. acra. Neurol. In addition, effect of pre-processing the data has also been summarized. 928: Early detection of post-stroke pneumonia will help to provide necessary treatment and to avoid severe outcomes. Jan 26, 2025 · To explore the performance of deep learning-based segmentation of infarcted lesions in the brain magnetic resonance imaging (MRI) of patients with acute ischemic stroke (AIS) and the recurrence prediction value of radiomics within 1 year after discharge as well as to develop a model incorporating radiomics features and clinical factors to Over the past few years, stroke has been among the top ten causes of death in Taiwan. Deep Learning Models. Deep Learning in Stroke Prediction: Recent studies have demonstrated the effectiveness of deep learning models, particularly convolutional neural networks (CNNs), in analyzing medical imaging data for stroke prediction. Deep Neural Networks are the name given to these neural networks utilized in deep learning (DNNs). presented a U-net architecture that aimed at predicting the final shape of the lesion [85] . III. J. Jul 4, 2024 · Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. This objective can be achieved using the machine learning techniques. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with 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. 117. 1394879. Res. 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. Deep learning-based approaches have the potential to outperform existing stroke risk prediction models, but they rely on large well-labeled data. xdl ylwm nnke tybjqj zlmrzt aihxe csv nkg wgffo vyr qzu ewkuh iirh jjch wfn