Diagnosis of heart disease using clinical data of cardiac patients

Abstract

Heart disease is a major global health concern and requires early and accurate diagnosis of effective treatment and prevention. Traditional methods often rely on manual interpretation, which can take a long time and be subjective. Automatic learning techniques can improve the accuracy, efficiency and objectivity of cardiology diagnosis by identifying complex data patterns, ensuring objective decision-making and enabling efficient data analysis. Automated learning applications in cardiology diagnosis include risk forecasting, medical imaging diagnosis and clinical decision support systems. It enables early identification of high-risk individuals, accurate interpretation of medical images and real-time clinical guidance. Health-care professionals, through the use of automated learning, can develop personal therapeutic plans that will lead to better outcomes for the patient and promote cardiovascular care.