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 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 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 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 2021 International Conference on Computer Communication and Informatics ICCCI written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 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 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 Decision Support Framework for Cardiovascular Disease Prediction Using Machine Learning written by Nitten Singh Rajliwall and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical decision making is an important and frequent task, which physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e., knowledge and experience learnt from experience, their research, related literature, patient cases, etc.) for anticipating or ascertaining health problems based on clinical risk factors, that deem to be the most salient. However, with the inundation of health data, from EHR system, wearable devices, and other systems for monitoring vital parameters, it has become difficult for physicians to make sense of this massive data, particularly, due to confounding and complex characteristics of chronic diseases, and there is a need for more effective clinical prediction approaches to address these challenges. Given the paramount importance of predictive models for managing chronic disease, cardiovascular diseases in particular, this thesis proposes a novel computational predictive modelling framework, based on innovative machine learning and data science approaches that can aid in clinical decision support. The focus of the proposed predictive modelling framework is on interpretable machine learning approaches that consist of interpretable models based on shallow machine learning techniques, such as those based on linear regression and decision trees and their variants, and model-agnostic approaches based on neural networks and deep learning methods but enhanced with appropriate feature engineering and post-hoc explainability. These approaches allow disease prediction models to be deployed in complex clinical settings, including under remote, extreme, and low-resource environments, where data could be small, big, or massive and has several inadequacies in terms of data quality, noise, or missing data. The availability of interpretable models, and model-agnostic approaches enhanced with explainable aspects are important for physicians and medical professionals, as it will increase transparency, trust and confidence in the decision support provided by computer based algorithmic models. This thesis aims to address the research gap that exists in the current ML/AI based disease detection models, particularly, the lack of robust, objective, explainable, interpretable and trustworthy inference available from the computer based decision support tools, with a majority of the performance metrics reported from computer based tools have been limited to quantitative measures such as accuracy, precision, recall, F-measure, AUC, ROC, without any detailed qualitative metrics, that provide insight into how the computer has arrived at a decision, and ability to explain the decision making logic, eliciting trust from the stakeholders using the system. This could be due to the problem that most of the current ML/AI tools were built using mathematically rigorous constructs, designed around black box approaches, which are hard to interpret and explain, and hence the decisions provided by them appear to be coming from a black box, offering little explanation on decision arrived. The research proposed in this thesis is aimed at the development of a breakthrough explainable predictive modelling framework, based on innovative ML/AI algorithms for building CVD disease detection models. The proposed computation framework provides an intelligent and interpretable holistic analytics platform with improved prediction accuracy, and improved interpretability and explainability. The proposed innovation and development can help drive the healthcare system to one that is more patient-centred, and trustworthy, with potential to be tailored for several diseases such as cancer, cardiovascular disease, asthma, traumatic brain injury, dementia, and diabetes. The outcomes of this research based on innovative findings can serve as an example - that the availability of better computer-based decision support tools, with novel computational strategies, which can address a patient's unique clinical/genetic characteristics, can result in better characterization of diseases and at the same time redefine therapeutic strategies. Some of the key contributions from this research include:• Novel disease detection models based on traditional shallow machine learning algorithms, particularly those based on decision trees and their variants. These algorithms have shown to be inherently interpretable and accurate white box models and can serve as the baseline for comparing with previous models proposed in the literature.• Innovative disease detection models based on model agnostic algorithms, such as deep learning networks, but augmented with appropriate pre- processing and post-processing stages to provide better interpretability and explainability and eventually make them an efficient white box model. For an objective comparison of the methods proposed in each of the above stages, several publicly available benchmark clinical datasets, including Cleveland dataset, NHANES dataset and Framingham Heart Study/CHS dataset were used for model building and experimental validation. Although Cardiovascular disease has been selected as the use case and disease under investigation, since it has led to an alarming increase in the burden of disease, almost at the epidemic levels, and is a major health concern in today's world, the findings from this research can lead to meaningful and significant impact towards improved self-management of chronic non-communicable diseases and make a significant contribution towards better public health management.

Download or read book Grammar Based Feature Generation for Time Series Prediction written by Anthony Mihirana De Silva and published by Springer. This book was released on 2015-02-14 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself. Industrial applications can use the proposed technique to improve their predictions.

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 Engineering High Quality Medical Software written by Antonio Coronato and published by IET. This book was released on 2018-02 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on high-confidence medical software in the growing field of e-health, telecare services and health technology. It covers the development of methodologies and engineering tasks together with standards and regulations for medical software.

Download or read book Advances in Internet Data and Web Technologies written by Leonard Barolli and published by Springer. This book was released on 2019-02-05 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents original contributions on the theories and practices of emerging Internet, Data and Web technologies and their applications in businesses, engineering and academia. As a key feature, it addresses advances in the life-cycle exploitation of data generated by digital ecosystem technologies. The Internet has become the most proliferative platform for emerging large-scale computing paradigms. Among these, Data and Web technologies are two of the most prominent paradigms, manifesting in a variety of forms such as Data Centers, Cloud Computing, Mobile Cloud, Mobile Web Services, and so on. These technologies altogether create a digital ecosystem whose cornerstone is the data cycle, from capturing to processing, analysis and visualization. The need to investigate various research and development issues in this digital ecosystem has been made even more pressing by the ever-increasing demands of real-life applications, which are based on storing and processing large amounts of data. Given its scope, the book offers a valuable asset for all researchers, software developers, practitioners and students interested in the field of Data and Web technologies.

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 Advances in Computing and Data Sciences written by Mayank Singh and published by Springer Nature. This book was released on 2020-07-17 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the 4th International Conference on Advances in Computing and Data Sciences, ICACDS 2020, held in Valletta, Malta, in April 2020.* The 46 full papers were carefully reviewed and selected from 354 submissions. The papers are centered around topics like advanced computing, data sciences, distributed systems organizing principles, development frameworks and environments, software verification and validation, computational complexity and cryptography, machine learning theory, database theory, probabilistic representations. * The conference was held virtually due to the COVID-19 pandemic.

Download or read book Computational Methodologies for Electrical and Electronics Engineers written by Singh, Rajiv and published by IGI Global. This book was released on 2021-03-18 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence has been applied to many areas of science and technology, including the power and energy sector. Renewable energy in particular has experienced the tremendous positive impact of these developments. With the recent evolution of smart energy technologies, engineers and scientists working in this sector need an exhaustive source of current knowledge to effectively cater to the energy needs of citizens of developing countries. Computational Methodologies for Electrical and Electronics Engineers is a collection of innovative research that provides a complete insight and overview of the application of intelligent computational techniques in power and energy. Featuring research on a wide range of topics such as artificial neural networks, smart grids, and soft computing, this book is ideally designed for programmers, engineers, technicians, ecologists, entrepreneurs, researchers, academicians, and students.