Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects.

this study aimed at evaluating the utilization of Support Vector Machine (SVM) in classifying patients of coronary heart disease with Unstable Angina Pectoris (UAP), Non-Segment (ST) Elevation Myocardial Infarction (NSTEMI) and ST-

2021年1月1日 · Data analytics to study correlations between health conditions and associated risk of coronary heart disease. Optimized traditional classifiers and ANNs cross-compared with ensemble counter-parts. Accuracy, precision, recall score, and AUC used were evaluvation of proposed architecture.

Narrowing the focus to a subset of 12 attributes, each annotated, a support vector machine (SVM) classifier is applied across different models—Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid.

Background and Objective: Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The early diagnosis and timely medical care of cardiovascular patients can greatly prevent death and reduce the cost of treatments associated with CAD. In this study, we attempt to prepare a new model for early CAD diagnosis.

2023年8月5日 · Therefore, a novel heart disease detection model based on the quantum-behaved particle swarm optimization (QPSO) algorithm and support vector machine (SVM) classification model, namely, QPSO-SVM, was proposed to analyze and predict heart disease risk.

2024年10月7日 · We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease...

2023年1月1日 · Cardiovascular diseases (CVDs) such as hypertension, heart failure, stroke, and coronary artery disease are now the major causes of early death worldwide, particularly in low and middle-income countries. Early detection of these disorders could lower the number of people who die prematurely.

Coronary heart disease (CHD) is one of the leading human diseases of high mortality in industrialized countries (1, 2). However, it is equally prevalent as a cause of death in de-veloping countries. Arisk factor is accepted as a causal factor if the results of observational studies and randomized con-

Objective: The present contribution concentrates on the application of support vector machines (SVM) for coronary heart disease and non-coronary heart disease classification.