Comprehensive Analysis of Factors Influencing Heart Disease Risk

Research Background and Significance: Heart disease continues to be a leading cause of mortality globally, posing significant challenges in the realms of prevention and management. The complexity of cardiovascular diseases arises from an interplay of genetic, lifestyle, and environmental factors, making their study and prediction critically important for public health. The integration of machine learning techniques into medical research has opened new avenues for understanding these diseases, significantly advancing the capabilities of predictive models. This paper leverages a comprehensive dataset from Kaggle, incorporating a diverse range of variables such as lifestyle habits and physiological markers known to influence cardiovascular health, which provides a foundation for robust analytical exploration. Contributions of This Paper: This study makes several significant contributions to the field of cardiovascular disease research. Firstly, it employs advanced statistical techniques such as Principal Component Analysis (PCA) and K-means clustering to effectively reduce data multicollinearity and dimensionality, which enhances the clarity and reliability of the findings. The PCA approach successfully condensed the data into principal components that explain a substantial portion of the variability, while K-means clustering categorized the data into meaningful risk profiles. Secondly, this paper demonstrates the utility of factor analysis in identifying major risk factors like smoking, age, and gender, furthering the understanding of their roles in heart disease risk. Finally, the application of various machine learning models.