Early Detection Of Cardiovascular Disease Research Articles

A Novel Convolutional Neural Network Deep Learning Implementation for Cuffless Heart Rate and Blood Pressure Estimation

Cardiovascular diseases (CVDs) affect components of the circulatory system responsible for transporting blood through blood vessels. The measurement of the mechanical force acting on the walls of blood vessels, as well as the blood flow between heartbeats and when the heart is at rest, is known as blood pressure (BP). Regular assessment of BP can aid in the prevention and early detection of CVDs. In the present research, a deep learning algorithm was developed to accurately calculate both blood pressure (BP) and heart rate (HR) by extracting relevant features from photoplethysmogram (PPG), electrocardiogram (ECG), and ABP signals. This algorithm was implemented using the Medical Information Mart for Intensive Care (MIMIC-II) dataset. It captures vital blood pressure-related features extracted from the PPG signal and accounts for the time relationship with the ECG. The algorithm also determines the values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on the ABP waveform through a convolutional neural network and stepwise multivariate linear regression. In comparison with other established BP measurement methods, our proposed approach achieved better results, with a mean absolute error (MAE) of approximately 4.7 mmHg for SBP and 2.1 mmHg for DBP, respectively. The standard deviation (STD) for SBP and DBP was approximately 7.6 mmHg and 3.9 mmHg, respectively. This study makes a valuable contribution to the healthcare field by introducing a novel, cost-effective continuous BP measurement method with improved accuracy while also minimizing the data dimension without losing any important information.

DS-CNN: Dual-Stream Convolutional Neural Networks-Based Heart Sound Classification for Wearable Devices

Cardiovascular diseases (CVDs) is considered a serious public health problem due to the uncertainty of its onset. Consuming wearable devices have increasing popularities for healthcare monitoring, and many of them are capable of continuous monitoring and early detection of CVDs. This paper proposes a framework for heart sound detection that can be considered for deployment on smart wearable devices to screen CVDs conveniently. A dual-stream convolutional neural network (DS-CNN) is developed to detect abnormal ones from short-term heart sound recordings. Preprocessing module is first employed for noise filtering and amplitude normalization. Then short-time Fourier transform and higher-order spectral are introduced for feature extraction, whose products are subsequently fed into the DS-CNN for screening abnormal heart sound signals. Two open accessible datasets are employed for performance evaluation. The results well demonstrate the classification accuracy of the proposed DS-CNN, and also indicate its advantages for adapting to heart sound recordings collected by different equipments.

Carbon-based light addressable potential sensor based on nitrogen-doped graphene quantum dots for detection of low-density lipoprotein

Cardiovascular diseases have become the number one killer of human health. Early diagnosis and treatment of cardiovascular diseases can effectively improve the treatment effect and reduce the disease death rate. As an important marker of cardiovascular disease, Low-Density Lipoprotein (LDL) can be used for the early detection of cardiovascular disease. In this study, a novel carbon-based Light Addressable Potentiometric Sensor (C-LAPS) for determination LDL has been constructed based on o-phenylenediamine modified nitrogen-doped graphene quantum dots/reduce graphene oxide (OPD@NGQDs/rGO) photosensitive layer. The OPD@NGQDs/rGO photosensitive layer has excellent photoelectric conversion efficiency, large specific surface area and good biocompatibility to enhance the response signal. For the determination LDL by the C-LAPS system, LDL binding to LDL aptamer causes a change in the surface potential of the photosensitive layer, producing a response signal that can be detected by the I-V curve. Under optimal conditions, the C-LAPS had a good linear relationship with LDL concentration in the range of 0.01 μg/mL- 0.15 μg/mL. The sensitivity was 1819 mV/µg·mL−1 and the limit of detection (LOD) was 6.43 ng/mL (S/N = 3). In addition, the C-LAPS showed good specificity, reproducibility, stability and satisfactory relative error for detecting LDL in human serum samples. Therefore, the C-LAPS has good performance and demonstrates potential clinical value.

IoT-Based Early Detection of Cardiovascular Disease with Ankle Brachial Index Measurement for Right and Left Body Simultaneously

IoT-Based Early Detection of Cardiovascular Disease with Ankle Brachial Index Measurement for Right and Left Body Simultaneously

Comparing emergency department visits 10-year apart at a tertiary care center in Lebanon.

Presentations to the emergency department (ED) are growing worldwide. With the increasing risk factors of non-communicable disease (NCD) and communicable diseases (CD) in low- and middle-income countries, it is crucial to understand how ED presentations are changing with time to meet patients' needs and allocate acute care resources. The aim of this study is to compare the changes in patient and diseases characteristics over 2 time periods 10 year apart at the largest tertiary care center in Lebanon. This was a retrospective descriptive study of patients presenting to the ED at a large tertiary care center in 2009/2010 and 2018/2019. The discharge diagnoses were coded into Clinical Classification Software codes. We used descriptive statistics, odds ratios (OR), and non-parametric test to compare the different diagnoses. The total number of ED visits increased by 33% from 2009/2010 to 2018/2019. The highest increase rate was among patients older than 65 years (2.6%), whereas the percentage of pediatric patients decreased from 30.8% to 25.3%. ED presentations shifted from NCD to CD. A shift in the discharge diagnoses was also noted within age groups, specifically a shift in cardiovascular diseases to a younger age. Our study suggests that the role of the ED is changing and moving towards treating the aging population and CD. There is a need to invest and mitigate CD, better allocate resources to accommodate the aging population, focus on awareness campaigns targeting early detection of cardiovascular diseases and modifying its risk factors.

Associations of lipid accumulation product, visceral adiposity index, and triglyceride-glucose index with subclinical organ damage in healthy Chinese adults.

Obesity is an independent risk factor for cardiovascular disease development. Here, we aimed to examine and compare the predictive values of three novel obesity indices, lipid accumulation product (LAP), visceral adiposity index (VAI), and triglyceride-glucose (TyG) index, for cardiovascular subclinical organ damage. A total of 1,773 healthy individuals from the Hanzhong Adolescent Hypertension Study cohort were enrolled. Anthropometric, biochemical, urinary albumin-to-creatinine ratio (uACR), brachial-ankle pulse wave velocity (baPWV), and Cornell voltage-duration product data were collected. Furthermore, the potential risk factors for subclinical organ damage were investigated, with particular emphasis on examining the predictive value of the LAP, VAI, and TyG index for detecting subclinical organ damage. LAP, VAI, and TyG index exhibited a significant positive association with baPWV and uACR. However, only LAP and VAI were found to have a positive correlation with Cornell product. While the three indices did not show an association with electrocardiographic left ventricular hypertrophy, higher values of LAP and TyG index were significantly associated with an increased risk of arterial stiffness and albuminuria. Furthermore, after dividing the population into quartiles, the fourth quartiles of LAP and TyG index showed a significant association with arterial stiffness and albuminuria when compared with the first quartiles, in both unadjusted and fully adjusted models. Additionally, the concordance index (C-index) values for LAP, VAI, and TyG index were reasonably high for arterial stiffness (0.856, 0.856, and 0.857, respectively) and albuminuria (0.739, 0.737, and 0.746, respectively). Lastly, the analyses of continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) demonstrated that the TyG index exhibited significantly higher predictive values for arterial stiffness and albuminuria compared with LAP and VAI. LAP, VAI, and, especially, TyG index demonstrated utility in screening cardiovascular subclinical organ damage among Chinese adults in this community-based sample. These indices have the potential to function as markers for early detection of cardiovascular disease in otherwise healthy individuals.

A Comprehensive Comparison of Six Publicly Available Algorithms for Localization of QRS Complex on Electrocardiograph.

The QRS complex is the most prominent feature of the electrocardiogram (ECG) that is used as a marker to identify the cardiac cycles. Identification of QRS complex locations enables arrhythmia detection and heart rate variability estimation. Therefore, accurate and consistent localization of the QRS complex is an important component of automated ECG analysis which is necessary for the early detection of cardiovascular diseases. This study evaluates the performance of six popular publicly available QRS complex detection methods on a large dataset of over half a million ECGs in a diverse population of patients. We found that a deep-learning method that won first place in the 2019 Chinese physiological challenge (CPSC-1) outperforms the remaining five QRS complex detection methods with an F1 score of 98.8% and an absolute sdRR error of 5.5 ms. We also examined the stratified performance of the studied methods on various cardiac conditions. All six methods had a lower performance in the detection of QRS complexes in ECG signals of patients with pacemakers, complete atrioventricular block, or indeterminate cardiac axis. We also concluded that, in the presence of different cardiac conditions, CPSC-1 is more robust than Pan-Tompkins which is the most popular model for QRS complex detection. We expect that this study can potentially serve as a guide for researchers on the appropriate QRS detection method for their target applications.Clinical Relevance-This study highlights the overall performance of publicly available QRS detection algorithms in a large dataset of diverse patients. We showed that there are specific cardiac conditions that are associated with the poor performance of QRS detection algorithms and may adversely influence the performance of algorithms that rely on accurate and reliable QRS detection.

A TWO-TIER MACHINE LEARNING FRAMEWORK FOR RISK ASSESSMENT IN DRIVERS WITH CARDIOVASCULAR DISORDERS

This work proposes a scheme based on a two-tier machine learning (ML) framework for the initial screening of commercial drivers with cardiovascular disorders prior to the actual driving assessment. First, the proposed framework aims to provide primary health care to cardiac drivers in resource-constrained scenarios such as bus terminals with the help of paramedical staff. The prediction of cardiovascular disease (CVD) in drivers is done using a variety of ML approaches, including Support Vector Machines (SVMs), Random Forests (RFs), Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), and XG-Boost (XGB). The K-fold cross-validation technique also tests the model’s ability to predict CVD. Second, a no-drive alert will be provided whenever the model predicts heart disease, and a comma-separated value (CSV) file stores the predicted abnormal parameters. An email-based data communication has been set up to transfer the CSV file. A MySQL database has been created to store the abnormal data received in hospitals which will help cardiologists with the proper diagnosis. This internet of medical things (IoMT) process will enable divers to come to the hospital for medication only when advised by a cardiologist, thereby reducing the burden of routine hospital visits. The Cleveland database of the UCI ML repository, a multivariate CVD dataset that contains 14 features from 303 people, is utilized to test the performance of the proposed model. Also, the proposed model performance is evaluated using two more publicly available heart disease datasets, i.e., the MIT-BIH arrhythmia dataset and the CVD dataset. The XGB, KNN, and RF ML techniques outperform state-of-the-art methods with performance accuracies of 88.53%, 91.8%, and 93.44%, respectively, for the Cleveland database; performance accuracies of 99.20%, 98.82%, and 99.08% for the MIT-BIH arrhythmia dataset; and performance accuracies of 73.29%, 69.48%, and 71.74% for the CVD dataset. Furthermore, the results showed comparable performance to the rest of the ML techniques. Early detection of CVD and consultation with specialist doctors are essential before it reaches a seriousness that can save drivers from vehicular accidents while seeking health care.

Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease Detection Using PPG Signals

Hypertension is the major root cause of blood pressure (BP) which in turn causes different cardiovascular diseases (CVDs). Hence BP need to be regularly monitored for preventing CVDs since it can be diagnosed and controlled through constant observation. Photoplethysmography (PPG) is identified as an important low-cost technology for facilitating a convenient and effective process in the early detection of CVDs. Different cardiovascular parameters such as blood oxygen saturation, heart rate, blood pressure, etc can be determined using the PPG technology. These cardiovascular parameters when given as input to the deep learning model is determined to diagnosis CVDs with maximized accuracy to an expected level. In this paper, Hybrid ResNet and Bidirectional LSTM-based Deep Learning Model (HRBLDLM) is proposed for diagnosing CVDs from PPG signals with due help in supporting the physicians during the process of continuous monitoring. This deep learning model mainly concentrated on the diagnosis of stage 1 hypertension, stage 2 hypertension, prehypertension, and normal CVDs with maximized accuracy using PPG signals. The PPG signals determined from PPG-BP dataset for investigation were recorded using IoT-based wearable patient monitoring (WPM) devices during the physical activity that includes high intensity, medium and low intensity movements involved driving, sitting and walking. The experiments conducted for this proposed deep learning model using PPG-BP dataset confirmed a better classification accuracy of 99.62% on par with the baseline PPG-based deep learning models contributed for detecting CVDs.

A Label-Free Electrochemical Biosensor for Homocysteine Detection Using Molecularly Imprinted Polymer and Nanocomposite-Modified Electrodes.

An essential biomarker for the early detection of cardiovascular diseases is serum homocysteine (Hcy). In this study, a molecularly imprinted polymer (MIP) and nanocomposite were used to create a label-free electrochemical biosensor for reliable Hcy detection. A novel Hcy-specific MIP (Hcy-MIP) was synthesized using methacrylic acid (MAA) in the presence of trimethylolpropane trimethacrylate (TRIM). The Hcy-MIP biosensor was fabricated by overlaying the mixture of Hcy-MIP and the carbon nanotube/chitosan/ionic liquid compound (CNT/CS/IL) nanocomposite on the surface of a screen-printed carbon electrode (SPCE). It showed high sensitivity, with a linear response of 5.0 to 150 µM (R2 of 0.9753) and with a limit of detection (LOD) at 1.2 µM. It demonstrated low cross-reactivity with ascorbic acid, cysteine, and methionine. Recoveries of 91.10-95.83% were achieved when the Hcy-MIP biosensor was used for Hcy at 50-150 µM concentrations. The repeatability and reproducibility of the biosensor at the Hcy concentrations of 5.0 and 150 µM were very good, with coefficients of variation at 2.27-3.50% and 3.42-4.22%, respectively. This novel biosensor offers a new and effective method for Hcy assay compared with the chemiluminescent microparticle immunoassay at the correlation coefficient (R2) of 0.9946.

Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction

The prevalence of cardiovascular diseases (CVD) makes it one of the leading reasons of death worldwide. Reduced mortality rates may result from early detection of CVDs and their potential prevention or amelioration. Machine learning models are a promising method for identifying risk variables. In order to make accurate predictions about cardiovascular illness, we would like to develop a model that makes use of transfer learning. Our proposed model relies on accurate training data, which was generated by careful Data Collecting, Data Pre-processing, and Data Transformation procedures.

Developing A Comprehensive Machine Learning Framework for Accurate Prediction and Cardiovascular Disease Risk

Cardiovascular diseases are one of the most fatal health conditions worldwide, posing a significant burden on national healthcare systems. Several risk factors, including high blood pressure, family history, stress, age, gender, cholesterol, BMI, and an unhealthy lifestyle, have been identified for these diseases. Early diagnosis is crucial for effective treatment, but the accuracy of existing approaches needs improvement due to the life-threatening nature of cardiovascular disorders. This study proposes a MaLCaDD framework for precise prediction of cardiovascular diseases. The methodology addresses missing data and imbalances, using Feature Importance to select relevant features, and an ensemble of Logistic Regression, SVM, Random Forest, Decision Tree, and KNN classifiers for accurate prediction. The study shows that MaLCaDD outperforms current state-of-the-art techniques, making it a trustworthy tool for early detection of cardiovascular diseases. The developed framework will be thoroughly tested and validated using different evaluation metrics, including sensitivity, specificity, and area under the curve, to ensure its accuracy and reliability. The ultimate goal of this project is to provide healthcare practitioners and policymakers with a reliable tool for predicting an individual's cardiovascular disease risk, which can help in early intervention and management, and ultimately reduce the burden of cardiovascular disease on society.

Evaluation of Machine Learning Techniques Towards Early Detection of Cardiovascular Diseases

The effectiveness of three Machine Learning (ML) algorithms: Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) techniques for the early diagnosis of heart diseases were evaluated. Heart disease’ dataset collected from kaggle.com data repository, which comprised of 303 data points with 13 features and a target variable were used and data preprocessing by data shuffling and dimension reduction were performed. The new dimension of the dataset was chosen such that 85.03% of the original information is retained. The preprocessed dataset was partitioned into 70% of the training set and 30% of the testing set. The ML algorithms were trained and tested for the diagnosis of cardiovascular diseases (CVD). The training performances of these models were evaluated with a k-fold cross-validation algorithm using 10 folds. The k-fold accuracy shows KNN with an accuracy of 0.837662, RF with an accuracy of 0.834091, and SVM with an accuracy of 0.814935. The test results also show KNN with an accuracy of 0.8, SVM with an accuracy of 0.7889, and RF with an accuracy of 0.7667. KNN emerged the best model both in training and test’s performances and is recommended for the early diagnosis of CVD.

Newly diagnosed diseases and the frequency of their occurrence in patients after a new coronavirus infection. Results of an International Register "Dynamics Analysis of Comorbidities in SARS-CoV-2 Survivors (ACTIV SARS-CoV-2)" (12-month follow-up)

Aim. To analyze newly diagnosed diseases and features of the post-COVID course in patients after a coronavirus disease 2019 (COVID-19) within 12-month follow-up.Material and methods. A total of 9364 consecutively hospitalized patients were included in the ACTIV registry. Enrollment of patients began on June 29, 2020, and was completed on March 30, 2021, corresponding to the first and second waves of the pandemic. Demographic, clinical, and laboratory data, computed tomography (CT) results, information about inhospital clinical course and complications of COVID-19 during hospitalization were extracted from electronic health records using a standardized data collection form. The design included follow-up telephone interviews with a standard questionnaire at 3, 6, and 12 months to examine the course of post-COVID period.Results. According to the ACTIV registry, 18,1% of patients after COVID-19 had newly diagnosed diseases (NDDs) over the next 12 months. Hypertension (HTN), type 2 diabetes and coronary artery disease (CAD) prevailed in the NDD structure. Comparison of the age-standardized incidence of NDDs (HTN, CAD, diabetes) in the post-COVID period in the ACTIV registry with NDD incidence in 2019 according to Rosstat and the expected incidence of NDDs according to the EPOHA study revealed that HTN, diabetes, CAD in patients after COVID-19 were registered more often as follows: HTN by 7,0 and 4,4 times, diabetes by 7,3 and 8,8 times, CAD by 2,3 and 2,9 times, respectively. NDDs most often developed in patients aged 47 to 70 years. Comparison of the actual and expected number of cases of newly diagnosed HTN, CAD and diabetes depending on age showed that the actual number of cases in the population of patients in the ACTIV register is significantly higher than expected for patients aged 45-69 years and for patients with hypertension or diabetes and aged <45 years. Patients with NDDs, compared with patients without NDDs, had a more severe acute COVID-19 course. Statistically significant independent predictors of NDD occurrence (HTN and/or diabetes and/or CAD) within 1 year after hospital discharge were age (direct relationship), body mass index (direct relationship) and glucose levels upon admission to hospital (direct relationship).Conclusion. Available evidence suggests that a strategy for managing COVID-19 survivors should include mandatory screening for early detection of cardiovascular disease and diabetes, which will be key to reducing the risk of further COVID-19 consequences.

The EVA Study: Early Vascular Aging in Women With History of Preeclampsia.

Background Early vascular aging (EVA) is associated with higher risk of adverse cardiovascular events and can be estimated noninvasively by assessing arterial hemodynamics. Women with a history of preeclampsia have increased risk of cardiovascular disease, but underlying mechanisms are incompletely understood. We hypothesized that women with a history of preeclampsia display persistent arterial abnormalities and EVA in the postpartum period. Methods and Results We performed a comprehensive, noninvasive arterial hemodynamic evaluation in women with a history of preeclampsia (n=40) and age-matched controls with previous normotensive pregnancies (n=40). We used validated methods integrating applanation tonometry with transthoracic echocardiography to obtain measures of aortic stiffness, steady and pulsatile arterial load, central blood pressure, and arterial wave reflections. Presence of EVA was defined as aortic stiffness higher than that predicted from reference values based on the participant's age and blood pressure. The association of preeclampsia with arterial hemodynamic variables was assessed with multivariable linear regression, and the association of severe preeclampsia with EVA was assessed with multivariable logistic regression, adjusted for confounders. We found that women with a history of preeclampsia had greater aortic stiffness, steady arterial load, central blood pressure, and arterial wave reflections when compared with controls. We observed a dose-response relationship, with the greatest abnormalities observed in subgroups with severe, preterm, or recurrent preeclampsia. Women with severe preeclampsia had 9.23 times greater odds of having EVA as compared with controls (95% CI, 1.67-51.06, P=0.011) and 7.87 greater odds of EVA as compared with women with nonsevere preeclampsia (95% CI, 1.29-47.77, P=0.025). Conclusions Our study comprehensively characterizes arterial hemodynamic abnormalities after preeclampsia and suggests that specific subgroups of women with a history of preeclampsia exhibit greater alterations in arterial hemodynamics related to arterial health. Our findings have important implications for understanding potential links between preeclampsia and cardiovascular events, and suggest women with severe, preterm, or recurrent preeclampsia as subgroups who may deserve intensification of efforts for prevention and early detection of cardiovascular disease.

An assessment of machine learning algorithms in diagnosing cardiovascular disease from right ventricle segmentation of cardiac magnetic resonance images

Healthcare research shows early detection of cardiovascular disease (CVD) is the key to saving the lives of patients suffering from CVDs. Furthermore, recent studies show the Right Ventricle (RV) is equally important as the Left Ventricle (LV) in diagnosing CVD. The research also shows it is difficult to assess the functioning of the RV in terms of eco-cardiograph because of the complexity of the RV position and structure of the heart. Assessing the RV for the accurate identification of echocardiographic parameters still remains the challenge for diagnosing CVDs. Machine learning (ML) and deep learning (DL) methods can produce accurate results from the images by segmenting them to reveal the clearer parts of the image for better identification or classification. This research aims to develop three algorithms: Vanilla Convolutional Neural Networks (Vanilla-CNN), Fourier-CNN (FCNN), and Residual Networks (ResNet) for the image segmentation of the images obtained from Magnetic Resonance Imaging (MRI) scan to extract the RV position. This helps in the identification of anomalies in heart and problems related to CVDs. The proposed algorithms are compared using standard performance metrics to evaluate their performance and viability. The results of DL algorithms reveal that all the flavours of CNN such as Vanilla CNN, FCNN and ResNet are capable to produce accurate results in image segmentation to identify CVDs.

Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method.

Cardiovascular disease is a serious health problem. Continuous Electrocardiograph (ECG) monitoring plays a vital role in the early detection of cardiovascular disease. As the Internet of Things technology continues to mature, wearable ECG signal monitors have been widely used. However, dynamic ECG signals are extremely susceptible to contamination. Therefore, it is necessary to evaluate the quality of wearable dynamic ECG signals. The topological data analysis method (TDA) with persistent homology, which can effectively capture the topological information of high-dimensional data space, has been widely studied. In this study, a brand-new quality assessment method of wearable dynamic ECG signals was proposed based on the TDA with persistent homology method. The point cloud of an ECG signal was constructed, and then the complex sequence was generated and displayed as a persistent barcode. Finally, GoogLeNet based on the transfer learning model with a 10-fold cross-validation method was used to train the classification model. A total of 12-leads ECGs Dataset and single-lead ECGs Dataset, established based on the 2011 PhysioNet/CinC challenge dataset, were both used to verify the performance of this method. In the study, 773 "acceptable" and 225 "unacceptable" signals were used as 12-leads ECGs Dataset. We relabeled 12,000 ECG signals in the challenge dataset, and treated them as single-lead ECGs Dataset after empty lead detection and balance datasets. Compared with the traditional ECG signal quality assessment method mainly based on waveform characteristics and time-frequency characteristics, the performance of the quality assessment method proposed. In this study, the classification performance of the proposed method are fairly great, mAcc = 98.04%, F1 = 98.40%, Se = 97.15%, Sp = 98.93% for 12-leads ECGs Dataset and mAcc = 98.55%, F1 = 98.62%, Se = 98.37%, Sp = 98.85% for single-lead ECGs Dataset.

Early Detection of Heart Disease Using Machine Learning Approach

Heart attack is one of the leading causes of morbidity in the worldwide population. Cardiovascular disease is one of the major diseases involved in clinical data analysis or one of the most important part for forecasting. Early detection of cardiovascular diseases can help to reduce high-risk condition for heart patients to make individual decisions for their lifestyle adjustments, mitigating the challenges. Early detection of heart disease has been explored in this study using a machine-learning approach. Additionally, we used sampling strategies to deal with disparate datasets. The overall risk is estimated using a variety of machine-learning techniques. On Kaggle, the Heart Disease dataset is accessible and open for all. In present study testing set used this dataset. The ultimate objective is to determine whether the patient has a “10-year risk of developing coronary heart disease” (CHD). The dataset contained thirteen features that provided patient data, and the authors used machine learning algorithms to diagnose cardiac problems with 98.8% accuracy.

Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning.

The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient's body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals' medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.

Cardiovascular Disease as a Consequence or a Cause of Cancer: Potential Role of Extracellular Vesicles.

Both cardiovascular disease and cancer continue to be causes of morbidity and mortality all over the world. Preventing and treating heart disease in patients undergoing cancer treatment remain an important and ongoing challenge for improving the lives of cancer patients, but also for their survival. Despite ongoing efforts to improve patient survival, minimal advances have been made in the early detection of cardiovascular disease in patients suffering from cancer. Understanding the communication between cancer and cardiovascular disease can be based on a deeper knowledge of the molecular mechanisms that define the profile of the bilateral network and establish disease-specific biomarkers and therapeutic targets. The role of exosomes, microvesicles, and apoptotic bodies, together defined as extracellular vesicles (EVs), in cross talk between cardiovascular disease and cancer is in an incipient form of research. Here, we will discuss the preclinical evidence on the bilateral connection between cancer and cardiovascular disease (especially early cardiac changes) through some specific mediators such as EVs. Investigating EV-based biomarkers and therapies may uncover the responsible mechanisms, detect the early stages of cardiovascular damage and elucidate novel therapeutic approaches. The ultimate goal is to reduce the burden of cardiovascular diseases by improving the standard of care in oncological patients treated with anticancer drugs or radiotherapy.