Download or read book Brain Stroke Prediction using Machine Learning Techniques A Comparative Study written by R. Balamurugan and published by GRIN Verlag. This book was released on 2023-10-05 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific Study from the year 2023 in the subject Computer Science - Bioinformatics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The use of machine learning for stroke prediction represents a powerful tool in enhancing patient care and reducing stroke-related mortality and disability. By focusing on key risk factors and leveraging extensive healthcare data, machine learning can substantially improve the accuracy and effectiveness of stroke prediction. This project aims to harness the potential of machine learning to better identify individuals at high risk of suffering a stroke and provide them with early, targeted interventions, ultimately saving lives and improving patient outcomes. The importance of predicting strokes cannot be overstated. Strokes are a leading cause of mortality and disability worldwide. Early detection and prevention can have a substantial impact on patient outcomes. Leveraging machine learning algorithms for stroke prediction can significantly improve the accuracy and efficacy of identifying high-risk patients. The primary objective of this project is to develop a precise stroke prediction system that can recognize high-risk patients based on a wide range of risk factors, including age, gender, medical history, lifestyle choices, and genetic factors. By creating a reliable model for stroke prediction, healthcare professionals can administer early interventions, potentially reducing stroke incidence and improving patient outcomes. The project's scope includes analyzing electronic health record (EHR) data to identify the key elements essential for stroke prediction. EHRs contain valuable information, including patient demographics, medical history, clinical findings, and other factors relevant to constructing a stroke prediction model. Machine learning for stroke prediction involves several stages. Initially, a dataset of relevant variables potentially influencing stroke occurrence is identified. This dataset may encompass demographic details, clinical information, laboratory tests, medical images, genetic data, and lifestyle factors. Subsequently, the dataset is cleaned and preprocessed to remove noise and inconsistencies. A machine learning algorithm is chosen, and the data is divided into training and testing groups. The algorithm is trained using the training data to identify patterns and relationships between variables and stroke occurrence. Once the model is trained, it is evaluated using the testing data to assess its performance.

Download or read book STROKE Analysis and Prediction Using Scikit Learn Keras and TensorFlow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-15 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to individuals and their likelihood of experiencing a stroke. We load the dataset and examine its structure, features, and statistical summary. Next, we preprocess the data to ensure its suitability for training machine learning models. This involves handling missing values, encoding categorical variables, and scaling numerical features. We utilize techniques such as data imputation and label encoding. To gain insights from the data, we visualize its distribution and relationships between variables. We create plots such as histograms, scatter plots, and correlation matrices to understand the patterns and correlations in the data. To improve model performance and reduce dimensionality, we select the most relevant features for prediction. We employ techniques such as correlation analysis, feature importance ranking, and domain knowledge to identify the key predictors of stroke. Before training our models, we split the dataset into training and testing subsets. The training set will be used to train the models, while the testing set will evaluate their performance on unseen data. We construct several machine learning models to predict stroke. These models include Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, and XGBoost. Each model is built and trained using the training dataset. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. With the models trained and saved, we move on to implementing the Python GUI. We utilize PyQt libraries to create an interactive graphical user interface that provides a seamless user experience. The GUI consists of various components such as buttons, checkboxes, input fields, and plots. These components allow users to interact with the application, select prediction models, and visualize the results. In addition to the machine learning models, we also implement an ANN using TensorFlow. The ANN is trained on the preprocessed dataset, and its architecture consists of a dense layer with a sigmoid activation function. We train the ANN on the training dataset, monitoring its performance using metrics like loss and accuracy. We visualize the training progress by plotting the loss and accuracy curves over epochs. Once the ANN is trained, we save the model to disk using the h5 format. This allows us to load the trained ANN for future predictions. In the GUI, users have the option to choose the ANN as the prediction model. When selected, the ANN model is loaded from disk, and predictions are made on the testing dataset. The predicted labels are compared with the true labels for evaluation. To assess the accuracy of the ANN predictions, we calculate various evaluation metrics such as accuracy score, precision, recall, and classification report. These metrics provide insights into the ANN's performance in predicting stroke. We create plots to visualize the results of the ANN predictions. These plots include a comparison of the true values and predicted values, as well as a confusion matrix to analyze the classification accuracy. The training history of the ANN, including the loss and accuracy curves over epochs, is plotted and displayed in the GUI. This allows users to understand how the model's performance improved during training. In summary, this project covers the analysis and prediction of stroke using machine learning and deep learning models. It encompasses data exploration, preprocessing, model training, hyperparameter tuning, GUI implementation, ANN training, and prediction visualization. The Python GUI enhances the user experience by providing an interactive and intuitive platform for exploring and predicting stroke based on various features.

Download or read book Machine Learning and Decision Support in Stroke written by Fabien Scalzo and published by Frontiers Media SA. This book was released on 2020-07-09 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book MACHINE LEARNING PREDICTION FOR POST STROKE DELIRIUM USING CLINICAL AND BRAIN REGIONAL CHARACTERISTICS OF ACUTE ISCHEMIC STROKE PATIENTS written by TAEYOUNG HA and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: We developed machine learning models to predict the occurrence of post-stroke delirium using the clinical and brain-regional characteristics of acute ischemic stroke patients.Method: We screened for delirium using the Confusion Assessment Method and used Diagnostic and Statistical Manual of Mental Disorders (5th edition) to diagnose delirium for the 675 consecutive acute ischemic stroke patients, who were admitted in stroke unit from August 2017 to July 2018. The clinical and brain imaging data of the patients were used to to test machine learning algorithms for the prediction of post-stroke delirium. We compared accuracy of machine learning algorithms including Support Vector Machine (SVM), Random Forest (RF) and Tree-based Gradient Boosting (XGBoost) performed with clinical and brain imaging. Results: Post-stroke delirium occurred in 66 (9.8%) of the total patients. On the comparison of the prediction accuracy of delirium occurrence, RF (93%) and XGBoost (92%) showed similar rates, and SVM (81%) was lower than two others. Top linked-variables to be included for the prediction of post-stroke delirium were age (feature importance, 1.50), National Institute of Health Stroke Scale (1.07) and modified Rankin Scale (0.73) at admission, side of old (0.69) and new stroke (0.47), size of new lesion (0.37), male (0.36), old infarction on cognition-related region (0.33). Conclusion: The present study shows the accuracy of the machine learning models to predict post-stroke delirium using the clinical and brain-regional characteristics of acute ischemic stroke patients. The top ranked variables could provide the possibilities to improve the prediction rate of post-stroke delirium.

Download or read book Precision Medicine in Stroke written by Ana Catarina Fonseca and published by Springer. This book was released on 2022-05-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive coverage of the state of the art in precision medicine in stroke. It starts by explaining and giving general information about precision medicine. Current applications in different strokes types (ischemic, haemorrhagic) are presented from diagnosis to treatment. In addition, ongoing research in the field (early stroke diagnosis and estimation of prognosis) is extensively discussed. The final part provides an in-depth discussion of how different interdisciplinary areas like artificial intelligence, molecular biology and genetics are contributing to this area. Precision Medicine in Stroke provides a practical approach to each chapter, reinforcing clinical applications and presenting clinical cases. This book is intended for all clinicians that interact with stroke patients (neurologists, internal medicine doctors, general practitioners, neurosurgeons), students and basic researchers.

Download or read book Neural Networks written by Raul Rojas and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

Download or read book Advanced Computing written by Deepak Garg and published by Springer Nature. This book was released on 2023-07-13 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes reviewed and selected papers from the 12th International Advanced Computing Conference, IACC 2022, held in Hyderabad, India, in December 2022. The 72 full papers and 6 short papers presented in the volume were thorougly reviewed and selected from 415 submissions. The papers are organized in the following topical sections: ​AI in industrial applications; application of AI for disease classification and trend analysis; design of agricultural applications using AI; disease classification using CNN; innovations in AI; system security and communication using AI; use of AI in human psychology; use of AI in music and video industries.

Download or read book Neural Information Processing written by Derong Liu and published by Springer. This book was released on 2017-10-27 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.

Download or read book Intelligent Systems Design and Applications written by Ajith Abraham and published by Springer Nature. This book was released on with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Interpretable Machine Learning Methods for Stroke Prediction written by Rebecca Zhang (S.M.) and published by . This book was released on 2019 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has long been touted as the next big tool, revolutionizing scientific endeavors as well as impacting industries like retail and finance. Naturally, there is much interest in the potential of next improving healthcare. However, using traditional machine learning approaches in this domain has many difficulties, chief among which is the issue of interpretability. We focus on the medical condition of stroke, a particularly desirable problem to target because it is one of the most prevalent and yet preventable conditions affecting Americans today. In this thesis, we apply novel interpretable prediction techniques to the problem of predicting stroke presence, location, acuity, and mortality risk for patient populations at two different hospital systems. We show that using an interpretable, optimal tree-based approach is roughly as effective if not better than black-box approaches. Using the clinical learnings from these studies, we explore new interpretable methodologies designed with medical applications and their unique challenges in mind. We present a novel regression algorithm to predict outcomes when the population is comprised of notably different subpopulations, and demonstrate that this gives comparable performance with improved interpretability. Finally, we explore new natural language processing techniques for machine learning from text. We propose an alternate end-to- end framework for going from unprocessed textual data to predictions, with an interpretable linguistics-based approach to model words. Altogether, this work demonstrates the promise that new parsimonious, interpretable algorithms have in the domain of stroke and broader healthcare problems.

Download or read book Artificial Intelligence Theory and Applications written by Harish Sharma and published by Springer Nature. This book was released on 2024-01-02 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features a collection of high-quality research papers presented at International Conference on Artificial Intelligence: Theory and Applications (AITA 2023), held during 11–12 August 2023 in Bengaluru, India. The book is divided into two volumes and presents original research and review papers related to artificial intelligence and its applications in various domains including health care, finance, transportation, education, and many more.

Download or read book Computational Intelligence for Engineering and Management Applications written by Prasenjit Chatterjee and published by Springer Nature. This book was released on 2023-04-29 with total page 925 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises select proceedings of the 1st International Conference on Computational Intelligence for Engineering and Management Applications (CIEMA - 2022). This book emphasizes applications of computational intelligence including machine intelligence, data analytics, and optimization algorithms for solving fundamental and advanced engineering and management problems. This book serves as a valuable resource for researchers, industry professionals, academicians, and doctoral scholars in engineering, production, thermal, materials, design, computer engineering, natural sciences, and management who work on computational intelligence. The book also serves researchers who are willing to use computational intelligence algorithms in real-time applications.

Download or read book Data Mining written by Ian H. Witten and published by Elsevier. This book was released on 2005-07-13 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output

Download or read book Machine Learning in Action Stroke Diagnosis and Outcome Prediction written by Ramin Zand and published by Frontiers Media SA. This book was released on 2022-08-18 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Building Machine Learning and Deep Learning Models on Google Cloud Platform written by Ekaba Bisong and published by Apress. This book was released on 2019-09-27 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Download or read book Brain inspired Machine Learning and Computation for Brain Behavior Analysis written by Rong Chen and published by Frontiers Media SA. This book was released on 2021-04-16 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi disciplinary Trends in Artificial Intelligence written by Rapeeporn Chamchong and published by Springer Nature. This book was released on 2019-11-06 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2019, held in Kuala Lumpur, Malaysia, in November 2019. The 19 full papers and 6 short papers presented were carefully reviewed and selected from 53 submissions. They cover a wide range of topics in theory, methods, and tools in AI sub-areas such as cognitive science, computational philosophy, computational intelligence, game theory, machine learning, multi-agent systems, natural language, representation and reasoning, data mining, speech, computer vision and the Web as well as their applications in big data, bioinformatics, biometrics, decision support, knowledge management, privacy, recommender systems, security, software engineering, spam filtering, surveillance, telecommunications, Web services, and IoT.