Cardiovascular disease

Background and Aim: Considering the possible role of psychological factors in the process of cardiovascular disease and also the fact that these factors interact with biological agents to produce effects on cardiovascular disease, the present study aimed at evaluation of the effectiveness of acceptance and commitment therapy on mental health, hopefulness and meaningfulness in people with cardiovascular disease. Materials and Methods: The present study was an applied one and was done as pre-test, post-test and control group. The study population included all cardiovascular patients who were referred to Isfahan Cardiovascular Research Center between January to March 2017 and had a history of myocardial infarction or open heart surgery in the last month. The study sample consisted of 30 patients with cardiovascular disease who were willing to participate in the study and met the inclusion criteria. The subjects were randomly assigned to the case and control groups (each group consisting of 15 subjects). Data were obtained using Snyder Hope Questionnaire, Meaning-seeking Questionnaire, and General Health Questionnaire and analyzed by repeated measure ANOVA and SPSS software. Results: The results showed that analysis of variance was significant for intragroup factor (time). For the intergroup factor, only significant variables were found for somatic symptoms and social dysfunction and were not significant for the other variables. The results of the present study indicate that time effect alone is significant regardless of group effect. The interaction of group and time was also significant (F = 12.84, df = 2) and its effect was reported 0.50. Conclusions: Overall, the findings of this study showed that acceptance and commitment based therapy is effective on mental health, hope and Meaning-seeking in Patients with Cardiovascular Diseases.

Purpose: Type 2 diabetes is one of the most important metabolic disorders that different societies are facing with increasing prevalence. Aerobic exercises are a common type of exercise that reduces the levels of triglycerides and low-density lipoproteins and regulates blood pressure. The purpose of the research is to investigate the effect of 12 weeks of aerobic exercise on fasting glucose and several serum indicators of cardiovascular disease, in women with type 2 diabetes. Methods: In 2023, 20 women who referred to the Hamadan Diabetes Association voluntarily participated as subjects in this research and were randomly assigned to aerobic (10 people) and control (10 people) groups. The exercise program of the aerobic group included 3 running sessions per week with an intensity of 60-70% of the maximum heart rate for 12 weeks. In order to measure fasting blood glucose, glycosylated hemoglobin (HbA1c) and lipid profile [low-density lipoprotein (LDL-c), triglycerides (TG), High-density lipoprotein (HDL-c)], blood sampling was done before and after 12 weeks of exercise program. SPSS software and Kologrov Smirnov and Student's t test were used to check and analyze the data. Results: The results indicated that after 12 weeks of aerobic exercise, HbA1c (P=0/027), LDL-c (P=0/012) and fasting blood glucose (P=0/043) decreased significantly in the aerobic group. But no significant changes were observed in HDL-c and BMI. Conclusion: The results of this research showed that performing aerobic exercises leads to a decrease in HbA1c, fasting blood glucose and improvement in lipid profile, so it can probably be a useful way of treatment and prevention of cardiovascular diseases in type 2 diabetic patients.

Cardiovascular-Diseases (CVD) are a principal cause of death worldwide. According to the World-Health-Organization (WHO), cardiovascular illnesses kill 20 million people annually. Predictions of heart-disease can save lives or take them, depending on how precise they are. The virus has rendered conventional methods of disease anticipation ineffective. Therefore, a unified system for accurate illness prediction is required. The study of disease diagnosis and identification has reached new heights thanks to artificial intelligence. With the right kind of training and testing, deep learning has quickly become one of the most cutting-edge, reliable, and sustaining technologies in the field of medicine. Using the University of California Irvine (UCI) machine-learning (ML) heart disease dataset, we propose a Convolutional-Neural-Network (CNN) for early disease prediction. There are 14 primary characteristics of the dataset that are being analyzed here. Accuracy and confusion matrix are utilized to verify several encouraging outcomes. Irrelevant features in the dataset are eliminated utilizing Isolation Forest, and the data is also standardized to enhance accuracy. Accuracy of 98% was achieved by employing a deep learning technique.

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.