Download or read book Making heart diseases detectable The invention of an algorithm for systematically predictions written by Daniyal Baig and published by GRIN Verlag. This book was released on 2020-11-17 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (postgraduate) from the year 2020 in the subject Computer Science - Programming, grade: 3, , course: Machine learning, language: English, abstract: In this research paper it will be conducted and experimentally analysed to seek an improved method to predict heart disease in the upcoming years. So efficient steps can be taken in order to predict and treat the avoidable fatal heart problem. This work will be creating an efficient algorithm which will detect the disease on the basis of some parameters and give as much accurate information as possible. By using this method one can systematically predict the risk of suffering from this disease. The main feature utilized in the detection will include age, gender, max heart rate, exercise induced angina etc. In today’s world the heart disease is increasing. Hence a lot of data related to the heart disease is being collected by using data mining. This important can be evaluated and used to predict and detect the coronary artery disease and heart related problem before the occurrence of the fatal experience. Many different types of life threating diseases are amongst people but heart disease has been studied the most in medical research. Early diagnosis of the disease is a very difficult task. We want to introduce an automated way of prediction of heart disease in individuals. This solution is not one and all solution but it will serve as a complementary diagnosis in the field of medical research. The main task in heart disease is to detect the disease early and treat it efficiently before any fatal experience occurs.
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 An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier written by Mohammad Ayoub Khan and published by Infinite Study. This book was released on with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal.
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 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 DC LG Algorithm for Increasing Efficiency in Heart Disease Prediction written by Gayathri. R and published by Mohammed Abdul Sattar. This book was released on 2023-11-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: According to WHO data, heart disease is to blame for one-third of all deaths globally each year. It is estimated that cardiovascular disease claims the lives of around 17.9 million people each year throughout the world. According to the European Cardiology Society(ECS), there are around 26 million people worldwide who have been diagnosed with cardiac illness, with an additional 3.6 million being diagnosed each year. In the first two years after diagnosis, around half of all patients with heart disease die and heart disease treatment accounts for about 3% of total health-care spending. To effectively predict heart illness, you'll need a slew of different tests. Improper forecasting may be the result of medical staff lacking sufficient expertise. It may be difficult to diagnose cancer at an early stage. The surgical treatment of heart disease is tough and this is much truer in developing countries that lack medical professionals, diagnostic equipment and other resources essential for accurate diagnosis and treatment of heart patients. It would help avoid catastrophic heart attacks and improve patient safety if cardiac failure risk could be precisely assessed. Machine learning algorithms can indeed be effective at detecting diseases provided they are properly taught with relevant data. To compare prediction models, there are publicly available datasets on heart disease. Scientists can now build the most accurate prediction model possible by combining machine learning and artificial intelligence, which are both on the rise. Cardiovascular Disease (CVD) mortality has been on the rise in both adults and children,
Download or read book Lecture Notes in Data Mining written by Michael W. Berry and published by World Scientific. This book was released on 2006 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited OC student-authored lecturesOCO which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms. The book''s discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining. Sample Chapter(s). Chapter 1: Point Estimation Algorithms (397 KB). Contents: Point Estimation Algorithms; Applications of Bayes Theorem; Similarity Measures; Decision Trees; Genetic Algorithms; Classification: Distance Based Algorithms; Decision Tree-Based Algorithms; Covering (Rule-Based) Algorithms; Clustering: An Overview; Clustering Hierarchical Algorithms; Clustering Partitional Algorithms; Clustering: Large Databases; Clustering Categorical Attributes; Association Rules: An Overview; Association Rules: Parallel and Distributed Algorithms; Association Rules: Advanced Techniques and Measures; Spatial Mining: Techniques and Algorithms. Readership: An introductory data mining textbook or a technical data mining book for an upper level undergraduate or graduate level course."
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 Information and Communication Technology for Competitive Strategies written by Simon Fong and published by Springer. This book was released on 2018-08-30 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 74 papers presented at ICTCS 2017: Third International Conference on Information and Communication Technology for Competitive Strategies. The conference was held during 16–17 December 2017, Udaipur, India and organized by Association of Computing Machinery, Udaipur Professional Chapter in association with The Institution of Engineers (India), Udaipur Local Center and Global Knowledge Research Foundation. This book contains papers mainly focused on ICT for Computation, Algorithms and Data Analytics and IT Security etc.
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 2020 IEEE International Conference for Innovation in Technology INOCON written by IEEE Staff and published by . This book was released on 2020-11-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Original contributions from researchers describing their unpublished research contribution which is not currently under review by another conference or journal and addressing state of the art research are invited to share their work in all areas 1 Data Science & Engineering 2 Computational Intelligence 3 Communication & Networking 4 Signal & Image Processing 5 RF Circuits, Systems and Antennas 7 Power, Energy and Power Electronics
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 Advanced Lectures on Machine Learning written by Olivier Bousquet and published by Springer. This book was released on 2011-03-22 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
Download or read book Multiple Classifier Systems written by Josef Kittler and published by Springer. This book was released on 2003-05-15 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.
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 Artificial Intelligence Application in Networks and Systems written by Radek Silhavy and published by Springer Nature. This book was released on 2023-07-08 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of artificial intelligence in networks and systems is a rapidly evolving field that has the potential to transform a wide range of industries. The refereed proceedings in this book is from the Artificial Intelligence Application in Networks and Systems session of the Computer Science Online Conference 2023 (CSOC 2023), which was held online in April 2023. The section brings together experts from different fields to present their research and discuss the latest trends and challenges. One of the key themes in this section is the development of intelligent systems that can learn, adapt, and optimize their performance in real time. Researchers are exploring how AI algorithms can be used to create autonomous networks and systems that can make decisions without human intervention. Furthermore, this section highlights the use of AI in improving network performance and efficiency. Researchers are exploring how AI algorithms can be used to optimize network routing, reduce congestion, and improve the quality of service. These efforts can help organizations save costs and improve user experience.
Download or read book Braunwald s Heart Disease E Book written by Peter Libby and published by Elsevier Health Sciences. This book was released on 2021-10-15 with total page 2473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current, comprehensive, and evidence-based Braunwald’s Heart Disease remains the most trusted reference in the field and the leading source of reliable cardiology information for practitioners and trainees worldwide. The fully updated 12th Edition continues the tradition of excellence with dependable, state-of-the-art coverage of new drugs, new guidelines, more powerful imaging modalities, and recent developments in precision medicine that continue to change and advance the practice of cardiovascular medicine. Written and edited by global experts in the field, this award-winning text is an unparalleled multimedia reference for every aspect of this complex and fast-changing area. Offers balanced, dependable content on rapidly changing clinical science, clinical and translational research, and evidence-based medicine. Includes 76 new contributing authors and 14 new chapters that cover Artificial intelligence in Cardiovascular Medicine; Wearables; Influenza, Pandemics, COVID-19, and Cardiovascular Disease; Tobacco and Nicotine Products in Cardiovascular Disease; Cardiac Amyloidosis; Impact of the Environment on Cardiovascular Health, and more. Features a new introductory chapter Cardiovascular Disease: Past, Present, and Future by Eugene Braunwald, MD, offering his unique, visionary approach to the field of cardiology. Dr. Braunwald also curates the extensive, bimonthly online updates that include "Hot Off the Press" (with links to Practice Update) and "Late-Breaking Clinical Trials". Provides cutting-edge coverage of key topics such as proteomics and metabolomics, TAVR, diabetocardiology, and cardio-oncology. Contains 1,850 high-quality illustrations, radiographic images, algorithms, and charts, and provides access to 215 videos called out with icons in the print version. Highlights the latest AHA, ACC, and ESC guidelines to clearly summarize diagnostic criteria and clinical implications. Provides tightly edited, focused content for quick, dependable reference. Flexible format options include either one or two volumes in print, as well as a searchable eBook with ongoing updates.
