Cardiovascular Diseases

Cardiovascular diseases are regarded as one of the most disabling diseases of human beings around the world, particularly when psychological characteristics are taken into consideration. This study compared attachment styles, problem solving styles and sensitivity anxiety in cardiovascular diseases patients and normal Individuals. A total of 40 participants (20 diseased, 20 normal) were selected thought random cluster sampling procedure from among a population of cardiovascular diseases in the city of Ardabil. Data were collected using the attachment styles inventory (AAI), problem-solving styles questionnaire (PSSQ) and the anxiety sensitivity index (ASI); moreover, multivariate analysis of variance used for data analysis. These finding implied that among attachment styles, problem solving and sensitivity anxiety there existed differences in cardiovascular diseases patients and non-patients. Results showed that cardiovascular diseases patients used higher avoidance and ambivalence attachment than non-patients. Result also revealed that non-patients used higher safety attachment than cardiovascular disease patients and patients employed avoidant attachment style as well as ambivalence. Moreover, cardiovascular diseases patients had higher helplessness, problem solving control and avoidance style more than non-patients and the non-patients used higher creativity style, problem-solving confidence, avoidance style more than cardiovascular diseases patients. It was also found that cardiovascular diseases patients used higher physical, cognitive, social worries than non-patients. Overall, the finding indicated that attachment styles, problem solving styles and sensitivity anxiety were important components discriminating cardiovascular diseases patients from non-patients. The suggestion for further studies is about other variables in cardiovascular diseases to provide preventive strategies for these diseases

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.

Heart disease has emerged as the foremost global cause of mortality. Enhancing awareness and understanding of this disease, along with preventive strategies, is pivotal in averting its onset and diminishing mortality rates. Today, social networks have evolved into paramount information dissemination platforms owing to their user-friendly nature and widespread adoption. Among these, Twitter stands out as a prominent source of rich data that can be leveraged for educational and awareness purposes. While existing studies have evaluated the impact of social media on increasing health-related knowledge, there is a gap in research regarding the role of Twitter in increasing cardiovascular disease awareness and prevention from different perspectives. This study employs a cross-sectional and descriptive methodology to quantitatively analyze over 50026 tweets.  This study seeks to investigate how Twitter users seek and disseminate information related to cardiovascular diseases. It aims to identify the prevalent topics shared about cardiovascular diseases and analyze the content of these messages.  Initially, 50026 tweets from 8,619 users were gathered over a one-month timeframe. English tweets have been selected due to the prevalence of the English language. Subsequently, the tweets were categorized and analyzed utilizing the LDA technique and the MALLET platform. Content analysis was conducted across various categories, focusing on topics, temporal trends, and geographical locations of the tweets. The results show that there was a significant relationship between the parameters extracted in the research and the most concern of users was in the field of heart diseases and prevention methods. Most user tweets (36,323 or 72.60%) contained specific information about heart disease. 9.33% related to cardiovascular issues, 2817 (5.63%) tweets were about heart attack, 2949 (5.89%) were about heart failure and 3267 (6.5%) about other cases related to heart disorders (cardiac arrest, cardiomyopathy, ischemic heart, etc.). The most concern of users in the group of heart diseases was related to the connection of topics such as cholesterol (4102 tweets (11.04%)), prevention (20348 tweets (56.01%)) and diet (1114 (3.06%)) with heart disease.