Exercise-induced Angina Research Articles

Heart disease prediction using machine learning models

Abstract. Heart disease remains a global health threat, making rapid identification crucial. Using the Heart Disease Dataset from Kaggle, this research employs a Random Forest model to analyze 13 clinical variables from 1,025 samples. To improve accuracy and address class imbalance, the dataset was split into training and test sets, utilizing methods such as Z-scores, SMOTE, and feature selection. The Random Forest model, which combines multiple decision trees, achieved high performance with an accuracy of 98.54%, identifying key predictors such as chest pain type, maximum heart rate, and thalassemia. Compared to a single decision tree, the Random Forest model reduces overfitting, improves generalization, and increases predictive accuracy. Factors like cholesterol levels, resting blood pressure, and exercise-induced angina were also considered. By averaging results from multiple trees, the model offers reliable and stable predictions, highlighting its potential in clinical settings for early detection and personalized treatment strategies. This study aims to assist healthcare providers in better allocating resources, planning preventive measures, and tailoring treatment plans to individual patients.

LOGISTIC REGRESSION TECHNIQUE FOR CARDIOVASCULAR DISEASE PREDICTION

Cardiovascular diseases (CVDs) are the most common cause of death in the world. Over four out of five CVD deaths are due to heart attacks and strokes. CVD high mortality has led to about 17 million deaths worldwide. Several machine and deep learning techniques are used to classify the presence and absence of CVD. This paper presents a logistic regression (LR) technique for predicting the risk of heart diseases (HD). The goal is to create an LR algorithm and build a prediction model that would foretell the development of HD. The dataset included data on 207 patients, featuring the following: age, sex, chest pain type, blood pressure, cholesterol levels, fasting blood sugar > 120 mg/dl, electrocardiogram results, maximum heart rate, exercise-induced angina, ST depression, slope of the ST segment, number of major vessels colored by fluoroscopy, and thallium scan results. Using this dataset to train the LR technique, a robust model was created to accurately predict the existence of HD in new patients. With an accuracy of 81%, a precision of 83%, and a recall score of 76%, the accuracy, precision, and recall key metrics were used to evaluate the model's efficacy. The model’s accuracy was compared to alternative methods, such as K-Nearest Neighbors and Decision Tree classifiers, which yielded accuracy of 81% and 76%, respectively. The obtained results are of great significance for healthcare providers – the proposed model can assist in identifying those who are at high risk of heart diseases and allow for early implementation of prophylactic...

A novel health monitoring system for vital signs using IoT

The current research looked at how to use the Internet of Things (IoT) to create a vital sign health monitoring system. Eight indications are employed to get critical patient information. Therefore, the number of nodes of the IoT embedded in the human body is 8, which have been worked on in different places of the body. Among the 8 nodes, node number 1 is located in the center of the grid (the center of the human body). The number of rounds is 9000 and the nodes are adopted with the initial energy of the nodes of 0.5 J and the radio range of 10 m. MATLAB software was used to simulate the WBAN network, which consists of IoT sensors embedded in the human body. The eight-item health assessment tool takes the following into account: pulse rate, blood pressure (mm Hg), serum cholesterol (mg/dl), temperature (°C), exercise-induced angina, and exercise-induced ST-wave depression, major blood vessels are counted using a medical procedure called endoscopy that involves examining the alveoli, which are small air sacs in the lungs where gas exchange occurs. We compared the number of major vessels at rest with the maximal heart rate during activity. The sensors were responsible for sending this data to the health center (base station). The data collected from the installation of these 8 sensors on 303 patients were collected and evaluated by machine learning method using MLP neural network method. Finally, it can be claimed that the present study has provided an automated method of determining the health of people using the IoT in a way that provides a state of health with an accuracy of over 99% and can be used in medical centers.

Intracoronary polarimetric signatures of exercise-induced vasospastic angina.

Intracoronary polarimetric signatures of exercise-induced vasospastic angina.

Comparison of Baseline and Post-Nitrate Exercise Testing in Patients with Angina but Non-Obstructed Coronary Arteries with Different Acetylcholine Test Results.

Background: Intracoronary acetylcholine testing may induce epicardial coronary artery spasm (CAS) or coronary microvascular spasm (CMVS) in patients with angina syndromes but non-obstructive coronary artery disease, but their causal role in individual patients is not always clear. In this prospective, observational single-center study, we aimed to assess whether (1) the induction of myocardial ischemia/angina by electrocardiogram (ECG) exercise stress test (EST) differs between patients showing different results in response to acetylcholine testing (i.e., CAS, CMVS, or no spasm); (2) the preventive administration of short-acting nitrates has any different effects on the EST of those patients who showed a positive basal EST. We expected that if exercise-induced angina and/or ischemic ECG changes are related to CAS, they should improve after nitrates administration, whereas they should not significantly improve if they are caused by CMVS. Methods: We enrolled 81 patients with angina syndromes and non-obstructive coronary artery disease, who were divided into three groups according to acetylcholine testing: 40 patients with CAS (CAS-group), 14 with CMVS (CMVS-groups), and 27 with a negative test (NEG-group). All patients underwent a basal EST (B-EST). Patients with a positive B-EST repeated the test 24-48 h later, 5 min after the administration of short-acting nitrates (N-EST). Results: There were no significant differences among the groups in terms of the B-EST results. B-EST was positive in eight (20%) patients in the CAS-group, seven (50%) in the CMVS-group, and six (22%) in the NEG-group (p = 0.076). N-EST, performed in eight, six, and five of these patients, also showed similar results in the three groups. Furthermore, the N-EST results also did not significantly differ compared to B-EST in any group, remaining positive in seven (87.5%), four (66.7%), and four (80%) patients in the CAS-group, CMVS-group, and NEG-group, respectively (p = 0.78). Conclusions: Our data show that patients with angina and non-obstructive coronary artery disease show largely comparable results of the ECG exercise stress test and similar poor effects of short-acting nitrates on abnormal ECG exercise stress test results. On the whole, our findings suggest caution in attributing to the results of Ach testing a definite causal role for the clinical syndrome in individual patients.

Exercise-Induced Vasospastic Angina With Prominent Regional Wall Motion Abnormality

Exercise-Induced Vasospastic Angina With Prominent Regional Wall Motion Abnormality

The Prediction of Heart Failure based on Four Machine Learning Algorithms

The main purpose of this study is to observe the correlations between clinical features (input) with heart disease (output) in the practice of machine learning and modeling. Primarily, this research conducts a data exploration of the attributes and the output to observe the relationships between the attributes and heart disease. The experiment method covers the Decision Tree, K-Nearest Neighbor, Support Vector Machine and Extreme Gradient Boosting algorithms after the data exploration. This research concludes that exercise-induced angina and ST depression are two factors that are highly related to heart disease. Older males should take more care of these features because they are more likely to have heart disease. Moreover, based on the output of four algorithms, the SVM is the best method for predicting the probability of the occurrence of heart disease since SVM output the highest values on the accuracy, recall rate, and f1-score.

Prediction Of Diseases in Smart Healthcare System Using Machine Learning

Smart healthcare systems rely heavily on disease prediction because it paves the way for early detection and prompt action, both of which enhance patient outcomes. In this research, we present a machine learning (ML) method for identifying data patterns that might be used to foretell the occurrence of cardiac disease. Our approach entails cleaning the data used for predicting cardiac issues and then using a Support Vector Machine (SVM). Age, sex, chest pain type, blood pressure, cholesterol, and exercise-induced angina are only few of the attributes included in the dataset. Insights into the distributional analysis of categorical and numeric variables, as well as potential connections and trends, are gained through exploratory data analysis (EDA). Cross-validation results show that the SVM model performs exceptionally well, with higher accuracy and AUC than competing models. By utilizing ML methods, our research aids in the development of intelligent healthcare systems. These results add to our understanding of how to forecast diseases and show how machine learning may transform healthcare systems to improve patient outcomes.

Assisted Artificial Intelligence Medical Diagnosis System for Heart Disease

In recent years increase of new and effective medical field applications has critical part in research. Artificial Intelligence (AI) Systems has great influence in the growth of these effective medical field applications and tools. One of the major health problems in both established and developing countries is heart disease. Therefore,diagnosis to regulate the heart disease is very vital, so that appropriate actions can be taken. The Artificial Intelligence System uses input medical data collected from an existing dataset from Kaggle and applies this data on the artificial intelligence application developed that uses data mining algorithm and a basic model on Zambian patients to see if the model will predict correctly. From the dataset collected 80% was used as training data and 20% was used as testing data. The Bayesian data mining algorithm was used for predicting the risk level and probability of heart disease. The system uses medical parameters to predict heart disease in patients and these parameters are age, sex, blood pressure, blood sugar (mg/dl), cholesterol (mm/dl),heart rate, exercise-induced angina, resting electrocardiogram, oldpeak, ST-slope and chest pain type. The data set collected by the system went through preprocessing which later supervised learning techniques and prediction model was conducted. Results were produced. Based on the results with the prediction accuracy of 90.97%, our results are in the same range as generated by other algorithms like KNN, Random Forest and Decision Tree algorithm.

Predicting the Chance of Heart Attack with a Machine Learning Approach – Supervised Learning

Machine learning is a multidisciplinary field combining statistics, computer science and artificial intelligence. This research finds a way to use machine learning to predict the chance of heart attack based on information about the patient. There are 13 features collected about each patient which are age, sex, cholesterol, chest pain type, maximum heart rate achieved, resting blood pressure, resting electrocardiographic results, fasting blood sugar, exercise-induced angina, previous peak, slope, number of major blood vessels, and thalassemia. The information of all the patients is put into a dataset. The dataset is split into two sets, one for training and another for validation. A computer model using a supervised learning algorithm is developed and trained to predict the chance of heart attack. During training, the training set is used for training the model, while the validation set is used for evaluating the accuracy of the model.

Utilizing Patient Characteristics to Predict the Likelihood of Heart Condition

As primary care providers deal with a multitude of chronic conditions, cardiovascular disease may often be overlooked. Finding cardiovascular disease early in a patient increases treatment options and reduces the likelihood of further complications. Thus, it is important that providers have tools at their disposal to assess patients’ odds of heart disease, given past medical histories and vital signs in clinic. Utilizing patient data from the Cleveland Heart Clinic, we developed a predictive model for assessing the odds that a patient evaluated in clinic may have an underlying heart condition. We selected each variable based on its relative capacity for prediction of heart condition, then further analyzed the contributes of this feature in the model. The final model includes the sex of the patient, their self-reported experience of exercise-induced angina, and resting ECG results. This model can be used in the clinic to estimate a patient’s odds of heart disease. Unlike current models used in primary care clinics, this model uses fewer variables and specifically assesses the odds of a current condition, as opposed to the future development of cardiovascular disease. Testing directly for heart disease is costly and time-consuming, so it’s useful to build a predictive diagnostic model.

Reactive Oxygen Species Cause Exercise-Induced Angina in a Myocardial Ischaemia-Reperfusion Injury Model.

Percutaneous coronary intervention (PCI) effectively treats obstructive coronary artery syndrome. However, 30–40% patients continue to have angina after a successful PCI, thereby reducing patient satisfaction. The mechanisms underlying persistent angina after revascularisation therapy are still poorly understood; hence, the treatment or guideline for post-PCI angina remains unestablished. Thus, this study aimed to investigate the mechanisms underlying effort angina in animals following myocardial ischaemia-reperfusion (I/R) injury. Phosphorylated extracellular signal-regulated kinase (p-ERK), a marker for painful stimulation-induced neuronal activation, was used for the investigation. After a forced treadmill exercise (FTE), the number of p-ERK-expressing neurons increased in the superficial dorsal horn of the I/R model animals. Moreover, FTE evoked hydrogen peroxide (H2O2) production in the I/R-injured heart, inducing angina through TRPA1 activation on cardiac sensory fibres. Notably, the treatment of a TEMPOL, a reactive oxygen species scavenger, or TRPA1−/− mice successfully alleviated the FTE-induced p-ERK expression in the dorsal horn. The production of H2O2, a reactive oxygen species, through physical exercise contributes to angina development following I/R. Hence, our findings may be useful for understanding and treating angina following revascularisation therapy.

Major adverse events associated with inducible cardiac ischemia during treadmill exercise testing for peripheral artery disease

BackgroundThe coexistence of coronary artery disease and peripheral artery disease (PAD) is well-established. Whether myocardial ischemia by electrocardiography during treadmill testing to evaluate PAD severity is associated with adverse cardiac and limb events has not been established. The aim of the current study is to assess the risk of major adverse cardiac events (MACE), major adverse limb events (MALE), and all-cause mortality in patients with evidence of myocardial ischemia on ECG compared with those without ischemia in patients undergoing treadmill testing for PAD evaluation. MethodsPatients undergoing treadmill exercise ankle-brachial index (ABI) evaluation (January 1, 2003, to December 31, 2006) were identified using the Mayo Clinic Gonda Vascular Laboratory database. Patients with ischemia by electrocardiogram (ECG) were age and sex matched to patients without ischemia. Outcomes were compared by ECG category. ResultsOf 4128 patients who underwent treadmill exercise, 170 (4.1%) had inducible myocardial ischemia by ECG. These were matched with 340 patients without ischemia. The positive ECG group had a higher percentage of diabetes mellitus (31.2% vs 21.8%; P = .02), carotid artery disease (22.4% vs 13.2%; P = .009), exercise-induced angina (14.1% vs 2.9%; P < .0001), and dyspnea (60.6% vs 35.6%; P < .0001). While the resting ABI was similar, the postexercise ABI was lower in the positive ECG group (0.5 vs 0.7; P = .04). After a median follow-up of 8 years, MACE were significantly greater in the positive ECG group (62.4% vs 46.5%; P < .001). MALE were significantly less frequent (17.1% vs 23.2%; P = .02), without an increased risk of amputation. In multivariable analysis, inducible ischemia was associated with higher incidence of MACE (hazard ratio, 1.65; 95% confidence interval, 1.25-2.16; P < .001) and lower incidence of MALE (hazard ratio, 0.51; 95% confidence interval, 0.31-0.84; P < .05). ConclusionsECG monitoring during vascular treadmill testing identified a subset of patients with more frequent MACE but less MALE.

A Rare Presentation of Angina and Arrhythmia in Absent Left Main Coronary Artery

Coronary artery anomalies (CAAs) are congenital disorders with multiple variations in the number, shape, and location of the Ostia of the coronary arterial system. The congenitally absent left main coronary artery (LMCA) is a rare anomaly that can present with benign or fatal complications ahead in life. Diagnosis and management of CAAs are sometimes challenging in low-risk patients.We present a unique case report of a 69-year-old Hispanic female who presented to the hospital with exercise-induced arrhythmia and angina symptoms. The patient complained of several episodes of chest pain, dizziness, and palpitations for a duration of two months. Electrocardiogram (EKG) and nuclear stress tests were equivocal. The angiogram revealed the separate origin of the left anterior descending artery (LAD) and left circumflex coronary artery (LCX) from the left coronary sinus. This anomaly should be considered in differentials when evaluating patients with angina symptoms.Congenital absence of LMCA is a rare condition that remains asymptomatic in the majority of the cases. It can present with exertional chest pain, palpitations, syncope, and sudden cardiac death (SCD). Occurrences of angina and arrhythmia should be carefully evaluated, and symptoms should be followed up closely. A coronary angiogram and electrophysiological testing can assist in the diagnosis.

The Prediction of Heart Disease u sing Machine Learning Technique

Medical errors are generally costly and harmful. There are many deaths worldwide every year. Clinical decision support systems provide opportunities to reduce medical errors and improve patient safety. Certainly, one of the most important aspects of applying such a system is the diagnosis and treatment of heart disease. Machine learning technology is implemented to analyze different kinds of heart-based problems. For this, this study essentially had two primary goals. Firstly, this paper intends to understand the role of variables in heart disease modeling better. Secondly, the study seeks to evaluate the predictive performance of the decision trees. Based on these results, first, men seem to be more susceptible to heart disease than women. Age, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, and the number of major vessels also show increased odds of having heart disease. Second, for the full model, the accuracy rate is 0.873, which implies that the error rate is 0.127. Among the patients who predicted not to have heart disease, the accuracy that would not have heart disease was 85.43%, and the accuracy that had heart disease was 89.10% among the patients predicted to have heart disease.

Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes

Heart disease is a disease with a high mortality rate in the world of health. The disease is usually rarely realized the cause. However, there are several parameters that can be used to predict whether a person has a risk of heart disease or not. As for this study, researchers will use several indicators including Age, Sex, Chest pain type, Trestbps, Cholesterol, Fasting blood sugar, Resting ECG, Max heart rate, Exercise-induced angina, Oldpeak, Slope, Number of vessels coloured, and Thal This research will perform calculations using the Data Mining method with the Naive Bayes Algorithm. The results of this study get an accuracy of 86% for the 303 datasets tested.

The Duke treadmill score with bicycle ergometer: Exercise capacity is the most important predictor of cardiovascular mortality.

BackgroundThe Duke treadmill score, a widely used treadmill testing tool, is a weighted index combining exercise time or capacity, maximum ST-segment deviation and exercise-induced angina. No previous studies have investigated whether the Duke treadmill score and its individual components based on bicycle exercise testing predict cardiovascular death.DesignTwo populations with a standard bicycle testing were used: 3936 patients referred for exercise testing (2371 men, age 56 ± 13 years) from the Finnish Cardiovascular Study (FINCAVAS) and a population-based sample of 2683 men (age 53 ± 5.1 years) from the Kuopio Ischaemic Heart Disease study (KIHD).MethodsCox regression was applied for risk prediction with cardiovascular mortality as the primary endpoint.ResultsIn FINCAVAS, during a median 6.3-year (interquartile range (IQR) 4.5–8.2) follow-up period, 180 patients (4.6%) experienced cardiovascular mortality. In KIHD, 562 patients (21.0%) died from cardiovascular causes during the median follow-up of 24.1 (IQR 18.0–26.2) years. The Duke treadmill score was associated with cardiovascular mortality in both populations (FINCAVAS, adjusted hazard ratio (HR) 3.15 for highest vs. lowest Duke treadmill score tertile, 95% confidence interval (CI) 1.83–5.42, P < 0.001; KIHD, adjusted HR 1.71, 95% CI 1.34–2.18, P < 0.001). However, after progressive adjustment for the Duke treadmill score components, the score was not associated with cardiovascular mortality in either study population, as exercise capacity in metabolic equivalents of task was the dominant harbinger of poor prognosis.ConclusionsThe Duke treadmill score is associated with cardiovascular mortality among patients who have undergone bicycle exercise testing, but metabolic equivalents of task, a component of the Duke treadmill score, proved to be a superior predictor.

Anomalous Left Coronary Artery From the Right Sinus of Valsalva in a Patient Presenting with ST-Segment Elevation in Lead aVR: A Case Report

BackgroundAnomalous coronary artery origins appear in roughly 1% of coronary angiograms, and up to 15% of syncope and sudden cardiac death events can be attributed to anomalous coronaries. Patients with an anomalous coronary artery arising from the opposite sinus may initially present with syncope and electrocardiographic findings of ischemia. Case ReportWe describe a case in which an adolescent male presented with exercise-induced angina and syncope, and his initial electrocardiogram (ECG) showed diffuse ST-segment depression with ST-segment elevation in lead aVR. Cardiac catheterization revealed there was no coronary ostium in the left coronary cusp, and the left coronary artery had an anomalous origin from the right cusp. The patient received urgent left internal mammary artery-to-left anterior descending artery coronary bypass and a saphenous vein graft to the ramus intermedius. After he underwent 6 months of medical therapy with β-blockade and angiotensin-receptor blockade, his left ventricular systolic function improved to low-normal level (left ventricular ejection fraction, approximately 50%). Why Should an Emergency Physician Be Aware of This?ST-segment elevation in lead aVR is strongly prognostic for left main or triple-vessel coronary artery disease. However, in patients who present with syncope and few other coronary artery disease risk factors, this ECG finding should be suggestive of an ischemic event caused by an anomalous left coronary artery. Early recognition of this pattern of clinical signs and ECG findings by an emergency physician could be critical for making the correct diagnosis and risk stratifying the patient for early coronary angiography and urgent surgical revascularization.

A Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Effect of Erenumab on Exercise Time During a Treadmill Test in Patients With Stable Angina.

ObjectiveTo determine the potential impact of erenumab, a human anti‐calcitonin gene‐related peptide (CGRP) receptor monoclonal antibody, on total exercise time (TET), time to exercise‐induced angina, and ST depression in a double‐blind, placebo‐controlled study in patients with stable angina due to documented coronary artery disease.BackgroundThe relative importance of the CGRP receptor pathway during myocardial ischemia has not been established.MethodsAn exercise treadmill test was conducted following a single IV infusion of erenumab 140 mg or placebo. The primary endpoint was the change from baseline in exercise duration as measured by TET with a noninferiority margin of −90 seconds. Safety follow‐up visits occurred through week 12. Eighty‐eight participants were included in the analysis.ResultsLS mean (SE) change in TET was −2.9 [14.8] seconds in the erenumab group and 8.1 [14.4] seconds in placebo; adjusted mean (90% CI) treatment difference was −11.0 (–44.9, 22.9) seconds. The CI lower bound (–44.9 sec) did not reach pre‐defined non‐inferiority margin of −90 seconds, demonstrating that TET change from baseline in the erenumab group was non‐inferior to placebo. There was no difference in time to exercise‐induced angina in erenumab and placebo groups (median [90% CI] time of 500 [420, 540] vs 508 [405, 572] seconds; hazard ratio [90% CI]: 1.11 [0.73, 1.69], P = .69) or time to onset of ≥1 mm ST‐segment depression (median [90% CI] time of 407 [380, 443] vs 420 [409,480] seconds; hazard ratio [95% CI]: 1.14 [0.76, 1.69], P = .59). Adverse events were reported by 27% and 32% of patients in erenumab and placebo groups.ConclusionsErenumab did not adversely affect exercise time in a high cardiovascular risk population of patients, supporting that inhibition of the canonical CGRP receptor does not worsen myocardial ischemia.

Microvascular Angina - Long-Term Exercise Stress Test Follow-up.

A sizeable proportion of patients with primary stable microvascular angina (MVA; exercise-induced angina, positive exercise stress test [EST], normal coronary arteries) have recurrent symptoms during follow-up. There have been no previous studies, however, on the long-term results of EST and their correlation with symptom outcome.Methods and Results:Follow-up EST was performed in 71 MVA patients at an average of 16.2 years (range, 5-25 years) from the first EST. Angina status was assessed on weekly frequency of angina episodes and nitroglycerin consumption and by whether symptoms had worsened, improved, or remained unchanged over time. At follow-up EST, 41 patients (group 1) had exercise-induced ischemia, whereas 30 patients (group 2) had negative EST. Compared to group 2, group 1 patients more frequently had exercise-induced dyspnea, and had a greater maximum ST-segment depression and a lower coronary blood flow response to adenosine and cold pressor test, but group 2 patients had a more frequent history of rest angina. No differences between the 2 groups were found at follow-up in angina status or change in symptom status during follow-up. Electrocardiogram results improve significantly in a sizeable proportion of patients with MVA. Changes in EST results, however, were not associated with clinical outcome.