Cardio-Vascular Disease Prediction using Machine Learning and Deep Learning

A major global health issue, cardiovascular disease (CVD) claims millions of lives annually and is a major cause of death. It is one of the most serious and dangerous conditions. It makes sense that accurate and prompt diagnosis could contribute to a decrease in death rates. Using variables including age, sex, blood pressure, and cholesterol levels, this study investigates machine learning and deep learning techniques for heart disease prediction. K-Nearest Neighbors, Support Vector Machines, and Decision Trees were among the conventional machine learning models used in the trials. These models were all tested for predicted accuracy, mostly on big datasets. However, we suggest using feature augmentation in conjunction with more sophisticated deep learning techniques for improved prediction accuracy. This paper provides insights that are expected to influence cardiovascular health by promoting early identification, enabling well-informed choices, and ultimately improving patient outcomes in the field of heart disease treatment