Download or read book Forecasting In Cardiology written by Ilja Stupelis and published by . This book was released on 1976 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Predicting Heart Failure written by Kishor Kumar Sadasivuni and published by John Wiley & Sons. This book was released on 2022-04-04 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 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 Forecasting in Cardiology written by Ilja Stupelis and published by John Wiley & Sons. This book was released on 1976 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Simulation Model for the Future Analysis of Cardiovascular Disease written by O. J. Vrieze and published by . This book was released on 1996-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A model to enable the user to design future scenarios pertaining to the incidence and cost of coronary heart disease, and to calculate the consequences of these scenarios. This text and diskette aim to analyze the future effects of strategic measures pertaining to coronary heart disease. It enables scientists, cardiologists, policy-makers and insurance companies to: design their own future scenarios; calculate the growth pattern of coronary heart disease in their region; and project the costs involved. The basis of the model is: the distribution of risk factors within the population; the effect of preventative measures on the distribution of risk factors; the effect this has on the risk of heart failure; the course of illness; the probability that a heart attack will result in death; therapeutic care; and the influence of treatment on the chance of survival, quality of life and the level of medical costs. The book describes the integral relationship between medical treatment, health effects and the future costs of coronary heart disease. The user-friendly program runs on any modern PC with a DOS operating system.
Download or read book AN EMPIRICAL STUDY AND ANALYSIS OF HEART DISEASE PREDICTION written by R. Subhashini 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: An empirical study and analysis of heart disease prediction involves using data analysis techniques to identify patterns and risk factors associated with cardiovascular disease. This approach utilizes machine learning algorithms to classify patients based on their likelihood of developing heart disease. The study involves collecting data on risk factors such as age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. Feature selection techniques are used to identify the most important risk factors, and a classification model is trained using these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC. This empirical study and analysis has several advantages, including the ability to identify new risk factors associated with heart disease, improved accuracy in predicting cardiovascular risk, and the potential to develop more personalized prevention and treatment strategies. This approach has the potential to improve medical decision-making and reduce the burden of heart disease on individuals and society.
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 Big Data in Cardiology Predicting Preventing and Managing Diseases written by Bikal Dhungel and published by GRIN Verlag. This book was released on 2020-10-28 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2020 in the subject Health Sciences - Health Logistics, grade: 1,7, Linnaeus University (School of Informatics), course: Information Systems, language: English, abstract: This study was conducted to analyze this process closer focusing on a case of Cardiology. Conducting a comprehensive literature review and qualitative expert interviews, the impact of big data in the field of Cardiology was explored. The result of the study shows that big data can play a positive role in three aspects: prediction of disease, prevention of disease and management of disease. Big data enables us to build models that can be used to predict the occurrence of disease. Based on this information, actions can be taken to prevent the disease. Data also helps to manage the disease by offering helpful insights. Medical personnel can retrieve the patient data, with the help of AI, they can make faster decisions allowing them to spend more quality time with the patients and reduce cognitive errors. Through the interviews, it was understood that even though the positive role of big data has been acknowledged, the implementation is still a challenge due to various limitations. The challenges lie mainly on technical know-how and domain knowledge. Further challenges were data security and privacy issues that need to be addressed to mitigate the risks that can be caused by them. The examples of big data implementation in various cases like in heart failure prediction or prevention shows a positive picture. The overwhelming majority of case studies analyzed in this regard show an optimistic picture. Due to growing importance and use of smart devices, IoT, genomics and the recent developments in the field of ICTs, it is expected that big data will not only leave a positive influence on the field of Cardiology, it will also change the way medicine is practiced and healthcare is offered. The statement ‘Data is the new oil’ has been broadly acknowledged due to its wide-ranging importance. Utilizing big data offers a variety of benefits. Although the health sector was late in terms of exploiting the benefits of big data, currently, the adoption is accelerating. Healthcare is increasingly becoming an information science and the implementation of electronic medical records (EMR) and other information systems is growing rapidly. The patient data originating from smart devices and other sources like genomic databases are supporting the healthcare sector offering better healthcare delivery and increasing efficiency, hence saving costs.
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 Heart Disease Forecasting in Healthcare written by Pathan Ahmed Khan and published by Ary Publisher. This book was released on 2023-03-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heart disease forecasting in healthcare is an important area of research that aims to predict and prevent cardiovascular diseases. The use of machine learning and artificial intelligence techniques to analyze large amounts of electronic health record (EHR) data has shown promising results in identifying risk factors, early detection, and treatment planning. The goal of heart disease forecasting is to improve clinical decision-making, reduce costs, and improve patient outcomes. Medical imaging and biomarkers also play a critical role in predicting heart disease, and researchers are exploring new ways to integrate these data sources with machine learning models. The use of precision medicine in heart disease forecasting can help personalize treatment plans for patients, based on their individual risk factors and genetic profiles. Heart disease forecasting has important implications for population health and chronic disease management, providing healthcare providers with a powerful tool to prevent and manage cardiovascular diseases, which remain a leading cause of death worldwide.
Download or read book Cardiac Data Mining for Heart Disease Prediction from Biomedical Warehouses written by Mujtaba Ashraf Qureshi and published by Independent Author. This book was released on 2023-04-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Cardiac Data Mining for Heart Disease Prediction from Biomedical Warehouses" by Mujtaba Ashraf Qureshi is a comprehensive study on the use of data mining techniques to predict heart disease using biomedical warehouses. Qureshi discusses the importance of data mining in healthcare and the potential benefits it can provide in the field of cardiac health. Through an extensive review of literature and experimentation, Qureshi explores the most effective data mining algorithms for heart disease prediction and examines the role of various risk factors. The author also discusses the challenges and limitations of cardiac data mining and offers potential solutions to overcome these obstacles. Whether you are a healthcare professional, researcher, or data scientist interested in the latest advancements in heart disease prediction, "Cardiac Data Mining for Heart Disease Prediction from Biomedical Warehouses" is an essential resource. Order your copy today and discover how data mining can help improve cardiac health outcomes.
Download or read book Contribution of Long Term Follow up to the Prediction of Coronary Heart Disease written by M. Kornitzer and published by S Karger Ag. This book was released on 1993-01-01 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Cardiac Arrest Prediction Using Machine Learning Model written by Dibakar Sinha and published by . This book was released on 2023-05-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Cardiac Arrest Prediction Using Machine Learning Model is a sophisticated system that leverages the power of machine learning algorithms to identify individuals who are at high risk of experiencing a cardiac arrest. This innovative solution aims to assist healthcare professionals in proactively identifying patients who may require immediate intervention or closer monitoring, thereby improving patient outcomes and potentially saving lives. The model is designed to analyze a variety of patient data, including medical history, vital signs, laboratory results, and other relevant clinical variables. By employing advanced machine learning techniques, the model learns patterns and relationships within the data to identify potential risk factors associated with cardiac arrest. The development of this model involves a two-step process. First, a comprehensive dataset is collected, consisting of anonymized patient information, including both historical data and real-time updates. This dataset is then used to train the machine learning model, which learns to recognize patterns and associations between different variables and the occurrence of cardiac arrest. Once the model is trained, it can be applied to new patient data in real-time. The system takes input from various sources, such as electronic health records, wearable devices, and continuous monitoring systems, to continuously assess a patient's risk of cardiac arrest. The model analyzes the incoming data and generates a prediction score or risk probability indicating the likelihood of a cardiac arrest event occurring within a specific timeframe. Healthcare professionals can utilize the prediction scores provided by the model to prioritize and allocate resources more efficiently. Patients identified as having a higher risk can receive immediate attention and proactive interventions to prevent cardiac arrest, such as medication adjustments, lifestyle modifications, or close monitoring in intensive care units. This targeted approach allows healthcare providers to intervene before the condition deteriorates, potentially improving patient outcomes and reducing mortality rates. The Cardiac Arrest Prediction Using Machine Learning Model is a promising advancement in healthcare technology, providing a proactive approach to cardiac care. By leveraging the power of machine learning algorithms and real-time patient data, it offers healthcare professionals valuable insights and tools to identify high-risk individuals, ultimately leading to improved patient care and better management of cardiac arrest risks. One needs both real-world experience and in-depth knowledge to make an accurate prediction of heart illness. Heart disease is now one of the most extremely dangerous and serious illnesses since it is difficult to diagnose. Thus, the ideal moment for both physicians and patients. Only when it can be correctly anticipated before a patient experiences a heart attack can cardiovascular illness be effectively diagnosed. This goal can be accomplished by combining a suitable machine learning approach with a significant volume of cardiovascular disease health information. In the modern digital era, data is an important resource, and a lot of data was being produced across many different businesses. The main origin of information in healthcare are data about the patients and information about illnesses. Tendencies in the sickness and provide individualised therapy for each patient by using healthcare information and ML techniques.
Download or read book A Novel Method for Heart Disease Prediction Using Feature Selection Method and Classification Algorithms written by C. Sowmiya and published by Independent Author. This book was released on 2023-02-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: a book by C. Sowmya that suggests a new approach to predicting cardiac disease using feature selection and classification algorithms.
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 Improving Early Detection and Risk Prediction in Heart Failure written by Vinicius Tragante and published by Frontiers Media SA. This book was released on 2022-06-03 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Untold Medical Story Coronary Blood Flow Heart Attack Prediction Prevention and Treatment written by Gunnar Sevelius M. D. and published by AuthorHouse. This book was released on 2011-04 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book describes Dr. Sevelius' long career as medical scientist, pursuing specifically what can be learned from a radiocardiogram (RCG), the recording through the skin of the heart flow, the cardiac output (CO), the most fundamental of all body functions. The RCG has been slow in acceptance in clinical medicine. One worry has been the radiation. The radiation is approximately one-third that of a chest x-ray and should be of minor concern with proper education. Another difficulty is how to interpret the results. Other techniques for CO measurements have had similar problems, not because the techniques were wrong but because the interpretation was based on wrong premises with too wide a standard deviation for proper diagnosis in clinical work. Dr. Sevelius introduces two new assessments: hemodynamic and metabolic. With these interpretations the heart as a pump is first judged according to the size of simultaneously measured blood volume it has to pump and second, separately, as to how large a body the heart has to supply with oxygen. The hemodynamic evaluation of the heart flow is found to be a good predictor to a within six-month pending heart attack. This would make the RCG an exceptionally simple and useful tool for diagnosis in clinical medicine. This book collects Dr. Sevelius' work in digital format to make it easily available. It is Dr. Sevelius' hope that his work will inspire some young scientists to follow up his work because of its wide application in modern medicine.
