Prognosis Of Heart Disease Research Articles

Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review

Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. If a forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method.

COVID-19 INFECTION IN PATIENTS WITH PROVEN HEART DISEASE

Objective: The COVID-19 pandemic remains a hot topic whose results are highly uncertain. This acute respiratory infection is mainly transmitted through respiratory droplets and contact routes. Frail patients such as those with severe heart disease are at higher risk of contracting moderate to severe forms of COVID-19 pneumonia. our goal was to determine the prevalence of COVID-19 in patients with proven heart failure and to give the result of this disease. Design and method: We collected data from the medical records of heart failure patients followed at the Medical and Cardiology Center over the past two years and looked for the onset of COVID-19 disease, risk factors for poor outcomes as well as the final outcome of the disease. Results: Out of 130 medical records, we recorded a total of 66 cases. The mean age of patients was 61.4 ± 17.8 years. The sex ratio was 0.81. LV systolic function was preserved in 47.7% of cases. COVID-19-related pneumonia was detected in 14.9% of cases; The clinical presentation was dominated by respiratory distress associated with moderate oxygen desaturation and fever. Forty-six patients (70.7%) were on renin-angiotensin-aldosterone inhibitors. Poor outcome factors were: BT systolic dysfunction (52.3%), atrial fibrillation (31.3%), pulmonary arterial hypertension (16.4%), impaired renal function (41.8%), impaired liver function (59.7%), and at least 10-fold increase in NT-Pro-BNP values (55.2%). In addition, LVH was observed in 32.8% of cases, complete LBBB in 13.4% of cases, and significant LV dilation in 55.2% of cases. There have been 8 deaths, including 5 related to COVID-19-related pneumonia. Survivors received long-term chest physiotherapy in addition to treatment for heart failure. Conclusions: Acute pneumonia due to COVID-19 can have a negative impact on the prognosis of heart disease. Thus, the factors of poor performance should encourage the isolation and/or treatment of these at-risk populations.

English

Early heart Disease prediction can reduce heart illness, which is a leading source of worldwide death. Unhealthy eating, mental stress, genetics, a sedentary lifestyle, and other factors have all contributed to the development of heart disease. Angina pectoris, dilated cardiomyopathy, stroke, and heart disease are most commonly caused by congestive heart failure. Moreover, a precise heart disease prognosis is necessary for effective cardiac treatment. In clinical machine learning, it is dangerously challenging to predict heart disease. This paper proposes a method based on Jellyfish Search Optimization (JSO) and enabled Deep Residual Networks (DRN) to improve heart disease prediction. The input dataset is subjected to the preprocessing step, which utilizes missing data imputation and Z- score Normalization. Consequently, the pre-processed output is fed to the feature selection stage, wherein features are selected by Kumar- Hassebrook similarity. At last, in the heart disease diagnosis phase, where DRN makes the heart disease identification, DRN is trained using the proposed JSO. Moreover, the proposed DRN_JSO has effectively delivered better performance parameters with high accuracy of 84.65% F1_score of 83. 65%, Matthews’s correlation coefficient (MCC) of 85.65% and a True Positive Rate (TPR) of 82.65%.

Cardiovascular Disease Prediction Using Machine Learning Ap-proaches

Cardiovascular disease is a prominent contributor to global mortality. The timely identification and prognostication of cardiovascular disease can mitigate its incidence and diminish fatality ratios. The use of machine learning has emerged as a promising methodology for forecasting the likelihood of heart disease. The present study delves into the application of machine learning algorithms in the prediction of heart disease. In this study, a publicly accessible dataset on heart disease is utilized to assess the efficacy of various machine learning algorithms and determine the optimal models. The study involves a comparative analysis of various algorithms, namely Logistic Regression, Random Forest, Support Vector Machines, and Artificial Neural Networks, with respect to their accuracy and other performance metrics. The findings indicate that the Artificial Neural Network model yielded the highest level of performance, exhibiting an accuracy rate of 87.5%. The aforementioned showcases the prospective employment of machine learning in the domain of heart disease prognosis, thereby highlighting the exigency for additional inquiry in this field.

SOCIO-DEMOGRAPHIC INDICATORS IN PATIENTS WITH CORONARY HEART DISEASE ACCOMPANIED BY DEPRESSION

Depressive disorders are more common both in the general population (more than 350 million people suffer from depressive disorders) and among patients with cardiovascular disease, especially coronary heart disease. The World Health Organization predicts that, by 2030, coronary heart disease and depressive disorders will play a leading role in disability and incapacity for work in the developed countries of the world. The prevalence of depressive disorders at the population level ranges from 2.5% to 10% [1, 2]. Depression is a risk factor for coronary heart disease, negatively influences the course of cardiovascular diseases, and adverse prognosis of the disease in such categories of people is a significant predictor [3, 4]. So, the Whitehall II study showed that the sharper the symptoms of depression, the higher the risk of coronary heart disease [5]. In addition, in patients suffering from cardiovascular diseases, the depressive state aggravates the clinical picture of the disease, and a direct link between the severity of depression and the risk of death has been established [6-11]. Because depression is not diagnosed and treated in time, patients often seek outpatient care and are hospitalized [11]. Due to complications of angina pectoris and many non-coronary complaints, patients' quality of life decreases. Thus, in patients with coronary heart disease in the postoperative period, against the background of depression, an additional decrease in their functional capabilities is revealed [12]. The negative influence of depressive disorders on the development of coronary heart disease is caused by behavioral and physiological mechanisms. The dysfunction of the hypothalamic-pituitary-adrenal, thyroid, and immune systems of the body and the disruption of serotonin, melatonin, and dopamine receptor activity are observed against the background of depression. One of the most important mechanisms of depression's effect on coronary heart disease prognosis is that sympathoadrenal system activation increases the risk of life-threatening cardiac rhythm disturbances [3, 5, 8, 13–15]. Depression significantly changes the behavior of patients with cardiovascular diseases and reduces their quality of life: patients do not want to follow their diet and treatment regimen, do not want to quit smoking or consuming alcohol, do not want to increase physical activity, and do not want to switch to a healthy lifestyle [16–19]. Often, depressive disorders are accompanied by anxiety. Anxiety increases the risk of coronary heart disease by about 41% and the risk of other cardiovascular diseases by up to 52% [20, 21], while increasing the risk of fatal cardiovascular cases [22] and reducing patients' social activity [23]. ]

Enhanced Heart Disease Prediction Based on Machine Learning and χ2 Statistical Optimal Feature Selection Model

Automatic heart disease prediction is a major global health concern. Effective cardiac treatment requires an accurate heart disease prognosis. Therefore, this paper proposes a new heart disease classification model based on the support vector machine (SVM) algorithm for improved heart disease detection. To increase prediction accuracy, the χ2 statistical optimum feature selection technique was used. The suggested model’s performance was then validated by comparing it to traditional models using several performance measures. The proposed model increased accuracy from 85.29% to 89.7%. Additionally, the componential load was reduced by half. This result indicates that our system outperformed other state-of-the-art methods in predicting heart disease.

Epidemiology and Characteristics of Surgical Intervention for Infective Endocarditis: A Population-Based Study in the Past Two Decades (2000 – 2019) in Hong Kong

Background The ‘inflamm-ageing’ phenomenon may worsen the prognosis of heart diseases including infective endocarditis (IE), while surgery may reduce inflammatory burden by direct removal of bacterial vegetation. This study aims to characterize the epidemiological and clinical features of surgical intervention for IE in Hong Kong in the past two decades. Methods All patients with incident IE from 2000-2019 in a territory-wide database were identified. We studied the trends in age-specific incidence of IE and identified the most significant contributors to 1-year all-cause death using the population attributable fraction (PAF). A propensity score approach was used to evaluate the benefits of surgical intervention. Results A total of 5,657 patients (mean age 60 years, 37% women) were included. Patients have become older, with an increasing trend in mean age from 2000-2019 (annual percentage change [APC] 0.9%, 95% CI 0.8 to 1.1, P Conclusions Patients with IE have become older in the past two decades, while surgical intervention significantly improved survival. Our results may help to improve outcomes of patients with IE by informing public health and clinical decisions.

An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT

In recent years, Internet of Medical Things (IoMT) and machine learning (ML) have played a major role in the healthcare industry and prediction of in time diagnosis of diseases. Heart disease has long been considered one of the most common and lethal causes of death. Accordingly, in this paper, a multiple-step method using IoMT and ML has been proposed for diagnosis of heart disease based on image and numerical resources. In the first step, transfer learning based on convolutional neural network (CNN) is used for feature extraction. In the second step, three methods of distributed stochastic neighbor embedding (t-SNE), F-score, and correlation-based feature selection (CFS) are utilized to select the best features. In the end, a combination of outputs of three classifiers including Gaussian Bayes (GB), support vector machine (SVM), and random forest (RF) according to the majority voting is employed for diagnosis of the conditions of heart disease patients. The results were evaluated on the two UCI datasets. The results indicate the improvement of performance compared to other methods.

The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction.

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

A STUDY ON MACHINE LEARNING TECHNIQUES USED FOR HEART DISEASE PREDICTION: A CRITICAL ANALYSIS

Among various diseases one of the hazardous diseases that takes uncountable lives every year is heart disease. In todays world, it is believed that one person dies from heart disease every minute. By WHO report number of deaths that take place due to heart disease is 1.75 million, with the number expected to rise to 75 million by 2030. As a result, an accurate and fast heart disease prognosis is a critical necessity for lowering heart disease-related death rates. The Angiography is the easiest and effective clinical test but it has drawback of expensive in cost and many side effects. So, in order to deal with this difficult issue, a significant role in various disease prognosis is shown by ICT. Machine learning is an emerging technology that performs automation from previous available data and shows promising results, it is a subset of artificial intelligence. In this work different literatures related to heart disease and machine learning techniques used in those literatures are critically analyzed and a review of them is summarized in a systematic manner.

Mitochondrial Fusion, Fission, and Mitophagy in Cardiac Diseases: Challenges and Therapeutic Opportunities.

Significance: Mitochondria play a critical role in the physiology of the heart by controlling cardiac metabolism, function, and remodeling. Accumulation of fragmented and damaged mitochondria is a hallmark of cardiac diseases. Recent Advances: Disruption of quality control systems that maintain mitochondrial number, size, and shape through fission/fusion balance and mitophagy results in dysfunctional mitochondria, defective mitochondrial segregation, impaired cardiac bioenergetics, and excessive oxidative stress. Critical Issues: Pharmacological tools that improve the cardiac pool of healthy mitochondria through inhibition of excessive mitochondrial fission, boosting mitochondrial fusion, or increasing the clearance of damaged mitochondria have emerged as promising approaches to improve the prognosis of heart diseases. Future Directions: There is a reasonable amount of preclinical evidence supporting the effectiveness of molecules targeting mitochondrial fission and fusion to treat cardiac diseases. The current and future challenges are turning these lead molecules into treatments. Clinical studies focusing on acute (i.e., myocardial infarction) and chronic (i.e., heart failure) cardiac diseases are needed to validate the effectiveness of such strategies in improving mitochondrial morphology, metabolism, and cardiac function. Antioxid. Redox Signal. 36, 844-863.

An Evaluation of Artificial Neural Networks and Random Forests for Heart Disease Prediction

Heart diseases are serious problem in many countries worldwide. In Malaysia, it has been a major killer since 1980. Many health conditions are closely related to heart disease. However, a large amount of data that medical centers have collected each year is not well-mined to find connections between them that can aid in the prognosis of heart disease. Therefore, the purpose of this study is to propose a predictive model of heart disease based on machine learning for prognosis to help individuals with symptoms to seek early advice and treatment. By following the Knowledge Discovery in Database (KDD) methodology that includes data selection, data pre-processing, data transformation, data mining, and interpretation or evaluation of acquired knowledge, this study has tested a dataset taken from UCI Machine Learning Repository. The classification of Artificial Neural Network and Random Forest was used. They were selected based on their adequacy in the medical field, particularly in the aspect of prognosis and diagnosis. The accuracy results obtained by the relevant works from previous authors are also high and reliable. This study uses a few ways to determine the maximum accuracy achieved by both algorithms: dataset splitting and K-Fold Cross-Validation. The results of the study on the test set that has been subdivided into several subsets showed that Artificial Neural Network and Random Forest produced stable accuracies by reaching 67.9% and 64.6%, respectively. The accuracy shown by the Artificial Neural Network is more stable for both subsets, training, and testing sets. In conclusion, Artificial Neural Network has been selected as the algorithm capable of working well with the Heart Disease Prediction Model, referring to the accuracy of the test set, which is slightly better than Random Forest.

Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence.

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.

Heart Disease Risk Prediction Expending of Classification Algorithms

Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today’s lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease the error rate and enhance the accuracy of HDP assessment measures. However, in order to discover the optimal HDP solution, this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California, Irvine (UCI) machine learning repository. In a comparative analysis, Mean Absolute Error (MAE), Relative Absolute Error (RAE), precision, recall, f-measure, and accuracy are used to evaluate Linear Regression (LR), Decision Tree (J48), Naive Bayes (NB), Artificial Neural Network (ANN), Simple Cart (SC), Bagging, Decision Stump (DS), AdaBoost, Rep Tree (REPT), and Support Vector Machine (SVM). Overall, the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate, with RAE of 33.2631 and MAE of 0.165, the precision of 0.841, recall of 0.835, f-measure of 0.833, and accuracy of 83.49 percent for the dataset gathered from UCI. The SC improves accuracy and reduces the error rate for the Kaggle dataset, which is 3.30% for RAE, 0.016 percent for MAE, 0.984% for precision, 0.984 percent for recall, 0.984 percent for f-measure, and 98.44% for accuracy.

Modified Look-Locker Inverse-Recovery (MOLLI) Sequence of Quantitative Imaging in Dirty Magnetic Resonance Longitudinal Relaxation Time Diagnostic Value of GE Combined with Longitudinal Relaxation Time Quantitative Imaging for Myocardial Amyloidosis.

The pathological changes of myocarditis include degeneration and necrosis of myocardial cells and infiltration of inflammatory cells in the myocardial interstitium, accompanied by obvious myocardial fibrosis. Myocardial fibrosis is a determinant of ventricular remodeling and an important indicator of the classification of clinical risk factors and has an important value in evaluating the prognosis of heart disease. Cardiac magnetic resonance (CMR) is the “gold standard” for evaluating the shape and function of the heart, and it can show the characteristic pathological changes of myocardial tissue. The traditional gadolinium imaging agent delays the enhanced sequence images to visually show the extent of the affected myocardial fibrosis, but it cannot effectively identify small focal fibrosis or widespread diffuse fibrosis. The CMR longitudinal relaxation time quantitative technique can directly measure the relaxation time (T1) determined by the myocardial tissue and does not depend on the signal strength of the reference tissue and can quantitatively analyze the affected myocardium. In this study, the initial and enhanced quantitative imaging techniques of CMR were used to measure the magnetic value of the myocardium in patients with myocarditis, to explore the diagnostic value of myocardial fibrosis, and to analyze the correlation between cardiac fibrosis and cardiac function.

Sarcopenia-derived exosomal micro-RNA 16-5p disturbs cardio-repair via a pro-apoptotic mechanism in myocardial infarction in mice

Sarcopenia is a pathophysiological malfunction induced by skeletal muscle atrophy. Several studies reported an association between sarcopenia-induced cardiac cachexia and poor prognosis in heart disease. However, due to lack of an established animal models, the underlying mechanism of disturbed cardiac repair accompanied with sarcopenia remains poorly understood. Here, we developed a novel sarcopenia-induced cardiac repair disturbance mouse model induced by tail suspension (TS) after cardiac ischemia and reperfusion (I/R). Importantly, we identified a specific exosomal-microRNA marker, miR-16-5p, in the circulating exosomes of I/R-TS mice. Of note, sarcopenia after I/R disturbed cardiac repair and raised the level of circulating-exosomal-miR-16-5p secreting from both the atrophic limbs and heart of TS mice. Likewise, miR-16-5p mimic plasmid disturbed cardiac repair in I/R mice directly. Additionally, in neonatal rat ventricular myocytes (NRVMs) cultured in vitro under hypoxic conditions in the presence of a miR-16-5p mimic, we observed increased apoptosis through p53 and Caspase3 upregulation, and also clarified that autophagosomes were decreased in NRVMs via SESN1 transcript interference-mediated mTOR activation. In conclusion, we show the pro-apoptotic effect of sarcopenia-derived miR-16-5p, which may be behind the exacerbation of myocardial infarction. Therefore, miR-16-5p can be a novel therapeutic target in the context of cardiac repair disturbances in sarcopenia–cachexia.

Analysis of heart rate variability in corn snakes (Pantherophis guttatus)

The assessment of heart rate variability (HRV) by linear methods in conjunction with Poincaré plots can be useful for evaluating cardiac regulation by the autonomic nervous system and for the diagnosis and prognosis of heart disease in snakes. In this report, we describe an analysis of HRV in conscious adult corn snakes Pantherophis guttatus (P. guttatus). The electrocardiogram (ECG) parameters were determined in adult corn snakes (8 females, 13 males) and used for HRV analysis, and the RR interval was analyzed by linear methods in the time and frequency domains. There was no sex-related difference in heart rate. However, significant differences were seen in the duration of the P, PR, and T waves and QRS complex; there was no difference in the QT interval. The values for the RR interval varied by 15.3% and 18.8% in male and female snakes, respectively, and there was considerable variation in the values for the high and low frequency domains. The changes in the time domain were attributed to regulation by the parasympathetic branch of the autonomic nervous system, in agreement with variations in the high and low frequency domains. The values for standard deviations 1 and 2 in Poincaré plots, as well as the values of the frequency domain, provide useful parameters for future studies of cardiac function in P. guttatus.

The REBECGA Brazilian Registry of Pregnancy and Heart Disease: Rationale and Design

Abstract Background: Maternal mortality rates in Brazil remain above the goals established by the United Nations Sustainable Development Goals. Heart disease is estimated to affect 4% of all pregnancies and remains by itself the main indirect obstetric cause of maternal death. In the last decades, a significant improvement in the prognosis of heart diseases has made pregnancy possible in women with heart disease and provided better maternal and fetal outcomes. Objective: To establish a multicenter Brazilian Registry of pregnant women with heart disease; to study the causes of immediate and late maternal mortality; and to assess the prevalence of heart disease in the country’s macro-regions. Method: This is an observational study, with retrospective and prospective stages, of the clinical and obstetric progression of pregnant women with heart disease. These women consecutively received care during pregnancy and will be followed up for up to a year after delivery at public and private hospitals with infrastructure for the execution of this project, a principal investigator, and approval by Ethics and Research Committees. Results: Our results will be presented after data collection and statistical analysis, aiming to demonstrate immediate and late maternal mortality rates, as well as the prevalence of heart disease in the country and its cardiovascular and obstetric complications during pregnancy. Conclusion: REBECGA will be the Brazilian Registry of heart disease and pregnancy and it will contribute to planning preventive measures, raising financial resources for the improvement of high-risk prenatal care, and reducing immediate and late maternal mortality due to heart disease.

Role of Brahma-related gene 1 (Brg1) in heart disease

Role of Brahma-related gene 1 (Brg1) in heart disease

WITHDRAWN: A comparative study on machine learning based heart disease prediction

Over the last few decades, the population worldwide is suffering from heart disease, which is considered one of the most significant fatalities. About 17.7 million people die on average every year because of heart disease, the World Health Organization (WHO). There will be many difficulties in the prognosis of heart disease due to various risk factors like diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and many other factors. The main goal is to save humans' lives by detecting abnormalities in heart conditions, which would be achieved by identifying and processing raw data collected based on heart information. The healthcare industry has found that Machine Learning (ML) is a useful and accurate decision-making technique in the data collection produced in large quantities. The medical decision support systems developed were effective based on the software and the different algorithms proposed by many researchers. Here a study is done based on the various techniques using the different algorithms and their performance analysis. The predicting model was introduced with several combined features, and among the multiple methods and were other classification techniques. Many existing ways discussed, among which the accuracy level was found as 88.7% using the Hybrid Random Forest with a Linear Model (HRFLM) technique.