Detecting Heart Attacks Using Learning Classifiers

Cardiovascular diseases (CVDs) have emerged as a critical global threat to human life. The diagnosis of these diseases presents a complex challenge, particularly for inexperienced doctors, as their symptoms can be mistaken for signs of aging or similar conditions. Early detection of heart disease can help prevent heart failure, making it crucial to develop effective diagnostic techniques. Machine Learning (ML) techniques have gained popularity among researchers for identifying new patients based on past data. While various forecasting techniques have been applied to different medical datasets, accurate detection of heart attacks in a timely manner remains elusive. This article presents a comprehensive comparative analysis of various ML techniques, including Decision Tree, Support Vector Machines, Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting, Multilayer Perceptron, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. These classifiers are implemented and evaluated in Python using data from over 300 patients obtained from the Kaggle cardiovascular repository in CSV format. The classifiers categorize patients into two groups: those with a heart attack and those without. Performance evaluation metrics such as recall, precision, accuracy, and the F1-measure are employed to assess the classifiers’ effectiveness. The results of this study highlight XGBoost classifier as a promising tool in the medical domain for accurate diagnosis, demonstrating the highest predictive accuracy (95.082%) with a calculation time of (0.07995 sec) on the dataset compared to other classifiers

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Evolutionary Psychology Meets Social Neuroscience

This book aims to open a debate full of theoretical and experimental contributions among the different disciplines in social research, psychology, neuroscience, and sociology and to give an innovative vision to the present research and future perspective on the topic. The fundamental research areas of evolutionary psychology can be divided into two broad categories: the basic cognitive processes, and the way they evolved within the species, and the adaptive social behaviors that derive from the theory of evolution: survival, parenting, family and kinship, interactions with nonparents, and cultural evolution. Evolutionary Psychology Meets Social Neuroscience explains at individual and group level the fundamental behaviors of social life, such as altruism, cooperation, competition, social exclusion, and social support

Imaginary relish and exquisite torture: The elaborated intrusion theory of desire

The authors argue that human desire involves conscious cognition that has strong affective connotation and is potentially involved in the determination of appetitive behavior rather than being epiphenomenal to it. Intrusive thoughts about appetitive targets are triggered automatically by external or physiological cues and by cognitive associates. When intrusions elicit significant pleasure or relief, cognitive elaboration usually ensues. Elaboration competes with concurrent cognitive tasks through retrieval of target-related information and its retention in working memory. Sensory images are especially important products of intrusion and elaboration because they simulate the sensory and emotional qualities of target acquisition. Desire images are momentarily rewarding but amplify awareness of somatic and emotional deficits. Effects of desires on behavior are moderated by competing incentives, target availability, and skills. The theory provides a coherent account of existing data and suggests new directions for research and treatment

from the immune system to neural networks

Storing memory of molecular encounters is vital for an effective response to recurring external stimuli. Interestingly, memory strategies vary among different biological processes. These strategies range from networks that process input signals and retrieve an associative memory to specialized receptors that bind only to related stimuli. The adaptive immune system uses such a specialized strategy and can provide specific responses against many pathogens. During its response, the immune system retains some cells as memory to act quicker when reinfections with the same or evolved pathogens occur. However, differentiation of memory cells remains one of the least understood cell fate decisions in immunology. The ability of immune memory to recognize evolved pathogens makes it an ideal starting point to study learning and memory strategies for evolving environments—a topic with applications far beyond immunology. In this thesis, I present three projects that study different aspects of memory strategies for evolving stimuli. Indeed, we find that specialized memory strategies can follow the evolution of stimuli and reliably recover memory of previous encounters. In contrast, fully connected networks, such as Hopfield networks, fail to reliably recover the memory of evolving stimuli. Thus, pathogen evolution might be the reason that the immune system produces specialized memories. We further find that specialized memory receptors should trade off their maximal binding for cross-reactivity to bind to evolved targets. To produce such receptors, the differentiation into memory cells in the immune system should be highly regulated. Finally, we study update strategies of memory repertoires using an energy-based model. We find that repertoires should have a moderate risk tolerance to fluctuations in performance to adapt to the evolution of targets. Nevertheless, these systems can be very efficient in distinguishing between evolved versions of stored targets and novel random stimuli.2022-01-2