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These studies suggest the medical term for heart disease is "cardiovascular disease (CVD)," which includes conditions like hypertension, coronary heart disease, cerebrovascular disease, peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease, and cardiomyopathies.
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The term "heart disease" encompasses a wide range of conditions affecting the heart and blood vessels. It is often used interchangeably with "cardiovascular disease" (CVD), which includes disorders such as coronary artery disease, hypertension, cerebrovascular disease, and cardiomyopathies . The terminology surrounding heart disease can be complex and varies across different medical dictionaries and classifications.
Cardiomyopathy is a specific type of heart disease that refers to diseases of the heart muscle. It is characterized by the heart muscle's abnormal condition, which is not due to known infectious agents. This term includes conditions like glycogen storage diseases, vitamin deficiencies, and endomyocardial fibrosis, but excludes ischemic heart disease. The term "cardiopathy" is a broader term that includes all diseases and disorders of the heart.
Heart failure (HF) is a clinical syndrome resulting from structural or functional cardiac abnormalities. It is classified into different stages and types based on the severity and left ventricular ejection fraction (LVEF). The stages range from at-risk for HF (Stage A) to advanced HF (Stage D), and the types include HF with reduced EF (HFrEF), mid-range EF (HFmrEF), preserved EF (HFpEF), and improved EF (HFimpEF).
Cardiovascular disease is a broad term that includes various disorders such as hypertension, coronary heart disease, cerebrovascular disease, peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease, and cardiomyopathies . These conditions affect the heart and blood vessels, leading to significant morbidity and mortality worldwide.
Machine learning (ML) algorithms are increasingly being used to predict heart disease. By analyzing patient data, such as the UCI database with 14 attributes, ML can improve the accuracy of heart disease predictions, potentially reducing the number of deaths by alerting patients to their risk. This approach highlights the importance of early detection and prevention in managing heart disease.
The medical term for heart disease encompasses a variety of conditions affecting the heart and blood vessels. Understanding the specific terminology, such as cardiomyopathy and heart failure classifications, is crucial for accurate diagnosis and treatment. Advances in machine learning offer promising tools for predicting and managing heart disease, emphasizing the need for continued research and collaboration across medical disciplines.
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