Cardiovascular Disease Analysis and Prediction Using Machine Learning Classifiers

The objective of this research is to use machine learning classifiers to predict cardiovascular disease. The model examines clinical data to identify individuals who are at high risk of cardiovascular events by utilizing algorithms such as Support Vector Machines (SVM), Random Forest, and Decision Trees. The classifiers are highly accurate in differentiating between patients who are considered healthy and those with medical conditions who are at risk since these algorithms are trained on characteristics like age, blood pressure, and cholesterol levels. This technique can significantly enhance patient outcomes in cardiovascular care by assisting medical professionals with early diagnosis and individualized treatment planning whenever implemented on a real-time health monitoring system.