Download or read book 2020 International Conference on Electronics and Sustainable Communication Systems ICESC written by IEEE Staff and published by . This book was released on 2020-07-02 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: International conference on Electronics and Sustainable Communication Systems (ICESC 2020) is one of the eminent conferences organized by Hindustan Institute of Technology, Coimbatore, India dedicated to drive innovation in nearly every aspect of electronic and communication systems The primary aim of ICESC 2020 is to promote the high quality and sustainable research works in an international platform of scientists, researchers, and industrialists by bringing together the state of the art research work in different facets of electronics and communication systems and discuss, share and exchange the research ideas under one common platform Prospective authors are invited to contribute and address different themes and topics of the conference

Download or read book Using Machine Learning to Predict Heart Disease written by Nikhil Bora and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heart Disease has become one of the most leading cause of the death on the planet and it has become most life-threatening disease. The early prediction of the heart disease will help in reducing death rate. Predicting Heart Disease has become one of the most difficult challenges in the medical sector in recent years. As per recent statistics, about one person dies from heart disease every minute. In the realm of healthcare, a massive amount of data was discovered for which the data-science is critical for analyzing this massive amount of data. This paper proposes heart disease prediction using different machine-learning algorithms like logistic regression, naïve bayes, support vector machine, k nearest neighbor (knn), random forest, extreme gradient boost, etc. These machine learning algorithm techniques we used to predict likelihood of person getting heart disease on the basis of features (such as cholesterol, blood pressure, age, sex, etc. which were extracted from the datasets. In our research we used two separate datasets. The first heart disease dataset we used was collected from very famous UCI machine learning repository which has 303 record instances with 14 different attributes (13 features and one target) and the second dataset that we used was collected from Kaggle website which contained 1190 patient's record instances with 11 features and one target. This dataset is a combination of 5 popular datasets for heart disease. This study compares the accuracy of various machine learning techniques. In our research, for the first dataset we got the highest accuracy of 92% by Support Vector Machine (SVM). And for the second dataset, Random Forest gave us the highest accuracy of 94.12%. Then, we combined both the datasets which we used in our research for which we got the highest accuracy of 93.31% using Random Forest. Keywords-- Heart Disease, Machine learning, naïve bayes, logistic regression, support vector machine, knn, random forest, extreme gradient boost

Download or read book Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning written by Rani, Geeta and published by IGI Global. This book was released on 2020-10-16 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Download or read book Predicting Heart Failure written by Kishor Kumar Sadasivuni and published by John Wiley & Sons. This book was released on 2022-04-05 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: PREDICTING HEART FAILURE Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it. This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. Predicting Heart Failure supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find: Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure Discussion of the risks and issues associated with the remote monitoring system Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations. Providing the latest research data for the diagnosis and treatment of heart failure, Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.

Download or read book Heart Disease Prediction Using Machine Learning Algorithms written by shu jiang and published by . This book was released on 2020 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is focused on the possibility of having heart disease by training four machine learning algorithms. By using the data provided by the UCI Machine Learning Repository, we can analyze and compare the models of logistic regression, random forest, extreme gradient boosting and neural network to choose the most robust model and determine important features in our model.

Download or read book 2020 IEEE Pune Section International Conference PuneCon written by IEEE Staff and published by . This book was released on 2020-12-16 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The scope of the conference includes Domains Tracks in the following key areas but not limited to only these areas The sessions are based on following fields and tracks, 1 Computer Vision and Machine Learning, 2 Electric vehicles, 3 Medical Signal Processing, 4 Assistive Technology, 5 Data Analytics

Download or read book Machine Learning and AI for Healthcare written by Arjun Panesar and published by Apress. This book was released on 2019-02-04 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

Download or read book Fundamentals and Methods of Machine and Deep Learning written by Pradeep Singh and published by John Wiley & Sons. This book was released on 2022-02-01 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Download or read book Image Processing and Capsule Networks written by Joy Iong-Zong Chen and published by Springer Nature. This book was released on 2020-07-23 with total page 829 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book emphasizes the emerging building block of image processing domain, which is known as capsule networks for performing deep image recognition and processing for next-generation imaging science. Recent years have witnessed the continuous development of technologies and methodologies related to image processing, analysis and 3D modeling which have been implemented in the field of computer and image vision. The significant development of these technologies has led to an efficient solution called capsule networks [CapsNet] to solve the intricate challenges in recognizing complex image poses, visual tasks, and object deformation. Moreover, the breakneck growth of computation complexities and computing efficiency has initiated the significant developments of the effective and sophisticated capsule network algorithms and artificial intelligence [AI] tools into existence. The main contribution of this book is to explain and summarize the significant state-of-the-art research advances in the areas of capsule network [CapsNet] algorithms and architectures with real-time implications in the areas of image detection, remote sensing, biomedical image analysis, computer communications, machine vision, Internet of things, and data analytics techniques.

Download or read book Machine Learning in Cardiovascular Medicine written by Subhi J. Al'Aref and published by Academic Press. This book was released on 2020-11-20 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine. Provides an overview of machine learning, both for a clinical and engineering audience Summarize recent advances in both cardiovascular medicine and artificial intelligence Discusses the advantages of using machine learning for outcomes research and image processing Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

Download or read book Machine Learning Based Heart Disease Diagnosis written by Pooja Rani and published by . This book was released on 2023-03-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Reliable and Accurate Heart Disease Prediction System written by G. Purusothaman and published by Ary Publisher. This book was released on 2023-03-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A reliable and accurate heart disease prediction system uses machine learning algorithms to predict the likelihood of heart disease based on a set of risk factors. This system utilizes decision tree, Naive Bayes, random forest, and support vector machine algorithms to analyze patient data and identify patterns that are indicative of cardiovascular disease. Feature selection techniques are used to identify the most important risk factors, which may include age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC. This system has several advantages, including improved accuracy in predicting heart disease risk, the ability to identify patients at high risk for cardiovascular disease, and the potential to integrate data from electronic health records and other sources. This approach has the potential to improve medical decision-making, provide more personalized care for patients, and reduce the burden of heart disease on individuals and society.

Download or read book 2019 1st International Conference on Innovations in Information and Communication Technology ICIICT written by IEEE Staff and published by . This book was released on 2019-04-25 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this conference is to create awareness and to provide a perfect platform for the participants to upgrade their knowledge and experience and to discuss on the ways to disseminate the awareness of the latest developments and advances in the field of Engineering & Technology This conference reflects the current focus of global research, recent developments, challenges and emerging trends in the field of Information and Communication Technologies

Download or read book A Novel Cluster And Rank Based Method For Prediction Of Heart Diseases written by K. Aravinthan and published by Ary Publisher. This book was released on 2023-03-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heart disease is a leading cause of death worldwide, and early prediction is crucial for effective prevention and management. A novel cluster and rank-based method for prediction of heart disease involves using machine learning algorithms to cluster patients based on similar risk factors and rank them based on their likelihood of developing cardiovascular disease. This method utilizes feature selection techniques to identify the most important risk factors and uses a classification model to predict the risk of heart disease based on these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC. This approach has several advantages, including improved accuracy in predicting heart disease risk, the ability to identify subgroups of patients with similar risk profiles, and the potential to integrate data from electronic health records and other sources.

Download or read book Revolutionizing Cardiac Muscle Detection Harnessing the Power of Machine Learning written by Yenni Rajasekhar and published by GRIN Verlag. This book was released on 2024-01-10 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bachelor Thesis from the year 2023 in the subject Medicine - Biomedical Engineering, , language: English, abstract: This analysis explores advanced deep-learning techniques for stock price prediction, assessing transfer learning-based DTRSI, CNNs, and collaborative networks with sentiment analysis. DTRSI effectively addresses overfitting, outperforming traditional models. CNNs excel in predicting stock trends across time frames, while collaborative networks combining sentiment analysis and candlestick data show promise, particularly for specific stocks over longer periods. The study investigates the relevance of sentiment analysis from platforms like Twitter and StockTwits in predicting market movements. It introduces an innovative active deep learning approach for stock price forecasting, considering data size and sector impact. Emphasizing LSTM-based models, it highlights their potential to enhance stock price forecasting, offering insights for traders and investors by consolidating diverse prediction methods. This research lays the groundwork for future studies optimizing trading systems via data integration and advanced neural network architectures.

Download or read book Glance of Machine Learning Algorithms for Prediction of Heart Disease written by Dr. P. Sujatha and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the digital world, data has turn out to be a most important asset for all kinds of organization. All industries are relying on the data for their day to day operations. We are living in the era of data driven decisions. Most of the industries are generating huge amount of data, Healthcare is one among the top. Health care industries generates large amount of data. The main challenge in healthcare professional is to analyze the data effectively for better treatment process. Modern approach in the health care is to prevent the disease with early diagnosis instead of treatment after diagnosis. It is possible to provide better healthcare services at lower costs and increase patient satisfaction with the current technologies. Big data analytics, data mining and machine learning are recent technologies used by healthcare professionals to analyze the data quickly and make better decisions on patient's treatment process. These technologies are used to automate the prediction and diagnosis of disease. Heart diseases are the most prominent reason for death in the world. In the last few decades heart diseases have emerged as life threatening disease. It is preventable if the disease is predicted in the early stage. Early prediction and diagnosis of heart disease can save human life. In recent years many research works have been done using data mining and machine learning algorithms to prevent and predict non communicable diseases like cancer, heart disease, stress, diabetes etc. The objective of this study is to provide glimpse about heart disease, types of heart disease, heart disease prediction factors and various Machine learning algorithms for heart disease prediction. This research paper is also analyzing various existing research works in machine learning algorithms to predict heart diseases.

Download or read book Machine Learning with Health Care Perspective written by Vishal Jain and published by Springer Nature. This book was released on 2020-03-09 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.