Suchergebnisse

Birth defects have become the important cause of mortality and morbidity in the perinatal period. Congenital heart disease (CHD) is the most common birth defect which includes the varying forms of cardiac abnormalities and occurs with an incidence of 1 per 100 live births. In most of the cases, CHD is an isolated malformation, but about 33% have associated anomalies. Ambiguous genitalia are one such rare anomaly that is associated with CHD among other genital abnormalities. The possible causes for this association could be pseudohermaphroditism, which in turn, may be due to congenital adrenal hyperplasia. The government of any country should consider providing for its people a free prenatal diagnosis for susceptible disorders.

Intro -- Title -- Copyright -- DEDICATION -- OTHER WORKS -- Table of Contents -- PREFACE -- FOREWORD by Dr. Clarence Lasby -- CHAPTER ONE: "Requiescat in Pace-Let Dignity Prevail" -- CHAPTER TWO: The Journey Begins -- CHAPTER THREE: Before the Fall -- CHAPTER FOUR: What Is A Heart Attack-And Why Did I Have One? -- CHAPTER FIVE: Catheterization-the Critical Tool -- CHAPTER SIX: "Marcus Welby, Where Are You When We Need You?" -- CHAPTER SEVEN: "It's Okay to Sweat Again" -- CHAPTER EIGHT Heart Disease and the Twentieth Century: -- CHAPTER NINE: Dick and Mike -- CHAPTER TEN: The Tools of the Electrophysiologist -- CHAPTER ELEVEN: Tinker Toys of the Cardiologist -- CHAPTER TWELVE: The Lotions and Potions of the Cardiologist -- CHAPTER THIRTEEN: "A Period of Stability -- CHAPTER FOURTEEN: The Elephant Returns (Or) "The Night They Drove Ol' Mikey Down" -- CHAPTER FIFTEEN "IT'S TIME…" -- CHAPTER SIXTEEN: WHAT LIES AHEAD -- CHAPTER SEVENTEEN: HEART DISEASE-THE GREAT EQUALIZER -- CHAPTER EIGHTEEN: "Getting to the Root of the Problem-Keeping Arteries Healthy with Diet and Drugs" -- CHAPTER NINETEEN: WHAT DOES IT ALL MEAN? -- CHAPTER TWENTY THE FINAL PERSPECTIVE -- ACKNOWLEDGEMENTS -- ABOUT THE AUTHORS.

In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Nearest Neighbor (KNN). The proposed work depends on the UCI database from the University of California, Irvine for the diagnosis of heart diseases. This dataset is preprocessed before running the machine learning model to get better accuracy in the classification of heart diseases. Furthermore, a 5-fold cross-validation operator was employed to avoid identical values being selected throughout the model learning and testing phase. The experimental results show that the Naive Bayes algorithm has achieved the highest accuracy of 97% compared to other ML algorithms implemented.

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential feature selection approach plays significant role in improving the performance of the proposed model. The proposed feature selection approach is evaluated using real world clinical heart disease dataset collected from University of California Irvine (UCI) data repository. Empirical test on validation set reveals that the proposed model performs well as compared to the existing methods. Overall, the state of-the-art heart disease detection model with classification accuracy of 98.53% is proposed for heart disease detection using SFS and random forest model.

Based on 35 nursing research articles, this article reports a methodological and substantive review of nursing research done between 1984 and 1993 regarding the impact of illness on families with a member experiencing ischemic heart disease. Limitations identified include lack of explicit conceptualization of family; implicit definitions of family restricted to the marital dyad; sampling procedures limited by convenience selection, gender, and elite bias; and data generated by individuals not interacting with other family members. Suggestions for future nursing research include integration of the growing body of family research methods, study of family strengths and coping over the process of disease progression, inclusion of the perspectives of children, and the impact on the family developmental life cycle.

The publication Information for Health provided a detailed exposition of the government's requirements for modernising the NHS from an information point of view.1 Furthermore, it described how information technology (IT) can be harnessed to support the process of patient care, involving the use of both the Electronic Patient Record (EPR) and Electronic Health Record (EHR). However, it is widely recognised that clinical computer systems in primary care are dramatically underutilised, and computerised patient records are of variable quality and reliability. One important factor has been the lack of training and support available to ensure greater use of IT (i.e. the clinical computer systems). Steps are being taken in Teesside to address this problem; the prime objective of which is to support practices to make greater use of their IT investment, and with particular reference to the national service framework (NSF) on coronary heart disease (CHD).2

SummaryThe variability of three behavioural risk factors for heart disease—heavy alcohol and tobacco consumption and physical inactivity—was assessed in an Australian Aboriginal community, where heart disease death rates were high. Prevalence levels were assessed by comparison with those experienced by all adult Australians and by evaluating whether Aboriginal rates were influenced by underlying sociodemographic conditions. Relative risk ratios, odds ratios and logistic regression analysis were used.A total of 159 males and 114 females participated. Compared to all Australians, Aborigines are significantly more likely to drink five or more drinks on a drinking day, to be current smokers, and not to participate in vigorous exercise. In the Aboriginal community, univariate analysis indicates that: the odds of being a heavy drinker are significantly higher for those in unsatisfactory health; odds of being a current smoker are significantly higher for those in unsatisfactory health or unemployed; odds of not participating in vigorous exercise are significantly higher for those in unsatisfactory health, unemployed or without secondary education. Multivariate analysis shows that risk of being a heavy drinker is independently associated with sex, age, and health status; risk of being a current smoker is associated with health and employment status. The risk of not participating in vigorous exercise is significantly related to all sociodemographic variables examined. Reasons for these associations are discussed.