Heart Health Insights: Analysing Data and Visualising Predictive Factors for Cardiovascular Disease

Cardiovascular Diseases (CVDs) are a leading cause of mortality worldwide, posing a significant public health challenge. This study aims to contribute to the existing research on CVD prediction by exploring the application of data analysis and visualization techniques. The researchers employed a range of machine learning algorithms, such as decision trees, support vector machines, random forest, and logistic regression, to analyze a comprehensive dataset of $\mathbf{1, 1 9 0}$ observations with 11 independent variables and the presence or absence of heart disease. The study focused on identifying the most significant factors contributing to the risk of heart disease using Principal Component Analysis (PCA) and evaluating the accuracy of different machine learning models in predicting the likelihood of CVDs. The findings offer important insights into how data analytics and visualization can be applied to detect and prevent cardiovascular diseases early on.